diff --git a/.hydra/hydra.yaml b/.hydra/hydra.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1dc3eac831dc5d775ce1fa02dd796978a9f76be4 --- /dev/null +++ b/.hydra/hydra.yaml @@ -0,0 +1,154 @@ +hydra: + run: + dir: ${oc.env:SCRATCH}/llm_negotiation/${now:%Y_%m}/${experiment.name} + sweep: + dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S} + subdir: ${hydra.job.num} + launcher: + _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][HYDRA] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + loggers: + logging_example: + level: DEBUG + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: RUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=RUN + task: [] + job: + name: generate_and_train + chdir: false + override_dirname: '' + id: ??? + num: ??? + config_name: ipd_prev_ad_align_qwen2.5_7b.yaml + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.2 + version_base: '1.1' + cwd: /home/mila/m/mohammed.muqeeth/llm_negotiation + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /home/mila/m/mohammed.muqeeth/llm_negotiation/configs + schema: file + provider: main + - path: '' + schema: structured + provider: schema + output_dir: /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b + choices: + hydra/env: default + hydra/callbacks: null + hydra/job_logging: default + hydra/hydra_logging: default + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: basic + hydra/output: default + verbose: false diff --git a/.hydra/overrides.yaml b/.hydra/overrides.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fe51488c7066f6687ef680d6bfaa4f7768ef205c --- /dev/null +++ b/.hydra/overrides.yaml @@ -0,0 +1 @@ +[] diff --git a/generate_and_train.log b/generate_and_train.log new file mode 100644 index 0000000000000000000000000000000000000000..5e47d52d84fd47e9644e2a669c05e245850d0d8c --- /dev/null +++ b/generate_and_train.log @@ -0,0 +1,87004 @@ +[2025-08-20 08:10:54,076][mllm.models.large_language_model_local][INFO] - Initializing adapter 'agent_adapter': no initial weights provided or found; starting from scratch. +[2025-08-20 08:10:55,185][mllm.models.adapter_training_wrapper][INFO] - Adapter 'agent_adapter': initialized with fresh weights (no initial weights found). +[2025-08-20 08:10:55,192][mllm.models.large_language_model_local][INFO] - Initializing adapter 'critic_adapter': no initial weights provided or found; starting from scratch. +[2025-08-20 08:10:56,011][mllm.models.adapter_training_wrapper][INFO] - Adapter 'critic_adapter': initialized with fresh weights (no initial weights found). +[2025-08-20 08:12:42,003][__main__][INFO] - Starting iteration 0. +[2025-08-20 08:13:04,747][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 66.42587245075853%, + +[2025-08-20 08:13:04,748][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 66.42587245075853%, + +[2025-08-20 08:13:04,754][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 66.42587245075853%, + +[2025-08-20 08:13:07,155][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 66.42587245075853%, + +[2025-08-20 08:13:07,157][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 66.42587245075853%, + +[2025-08-20 08:13:07,163][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 66.42587245075853%, + +[2025-08-20 08:13:07,165][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:13:07,166][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:13:07,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:08,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:09,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:10,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:11,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:12,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:12,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:13,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:14,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:15,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:15,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:16,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:17,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:18,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:19,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:20,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:21,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:22,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:22,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:23,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:24,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:25,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:25,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:26,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:27,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:28,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:29,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:29,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:30,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:31,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:32,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:33,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:13:34,520][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 0.6203231811523438 GB, + ΔVRAM Reserved: 0.9375 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:13:35,498][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:13:35,500][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:13:36,932][__main__][INFO] - Iteration 1 took 54s (36.99% Gen, 63.01% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 12m 23s. Estimated total time: 15h 15m 29s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 44s. +[2025-08-20 08:13:36,934][__main__][INFO] - Starting iteration 1. +[2025-08-20 08:13:59,997][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:13:59,998][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:14:00,004][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:14:02,451][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:14:02,452][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:14:02,458][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:14:02,461][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:14:02,461][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:14:02,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:03,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:04,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:05,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:05,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:06,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:07,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:08,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:09,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:09,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:11,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:11,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:12,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:13,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:14,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:15,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:15,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:16,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:17,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:18,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:19,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:19,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:20,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:21,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:22,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:23,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:23,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:24,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:25,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:26,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:26,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:27,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:14:29,408][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:14:30,876][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:14:30,878][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:14:32,233][__main__][INFO] - Iteration 2 took 55s (37.34% Gen, 62.66% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 17m 36s. Estimated total time: 15h 21m 37s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 9s, 500 more iterations: 7h 40m 48s. +[2025-08-20 08:14:32,234][__main__][INFO] - Starting iteration 2. +[2025-08-20 08:14:58,832][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:14:58,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:14:58,840][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:01,289][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:01,290][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:01,297][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:01,299][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:15:01,299][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:15:01,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:02,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:03,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:03,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:04,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:05,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:06,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:07,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:07,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:08,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:09,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:10,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:11,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:11,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:12,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:13,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:14,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:15,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:15,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:16,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:17,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:18,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:19,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:19,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:20,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:21,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:22,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:23,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:24,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:25,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:25,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:26,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:28,361][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:15:29,335][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:15:29,337][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:15:30,690][__main__][INFO] - Iteration 3 took 58s (41.34% Gen, 58.66% Train). Generation: 24s, Training: 34s. Estimated remaining time: 16h 9m 16s. Estimated total time: 16h 14m 15s. Time estimates for 10 more iterations: 9m 44s, 100 more iterations: 1h 37m 25s, 500 more iterations: 8h 7m 7s. +[2025-08-20 08:15:30,692][__main__][INFO] - Starting iteration 3. +[2025-08-20 08:15:53,773][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:53,774][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:53,781][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:56,231][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:56,232][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:56,239][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:15:56,241][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:15:56,241][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:15:56,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:57,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:58,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:58,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:15:59,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:00,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:01,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:02,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:02,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:03,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:04,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:05,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:06,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:06,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:07,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:08,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:09,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:10,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:10,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:11,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:12,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:13,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:14,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:15,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:16,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:16,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:17,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:18,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:19,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:20,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:20,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:21,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:23,288][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:16:24,242][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:16:24,244][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:16:25,563][__main__][INFO] - Iteration 4 took 54s (37.61% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 8m 35s. Estimated total time: 15h 14m 30s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 15s. +[2025-08-20 08:16:25,564][__main__][INFO] - Starting iteration 4. +[2025-08-20 08:16:49,103][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:16:49,105][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:16:49,111][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:16:51,569][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:16:51,570][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:16:51,576][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:16:51,578][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:16:51,579][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:16:51,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:52,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:53,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:54,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:55,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:55,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:56,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:57,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:58,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:59,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:16:59,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:00,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:01,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:02,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:02,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:03,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:04,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:05,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:06,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:06,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:07,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:08,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:09,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:10,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:11,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:12,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:12,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:13,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:14,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:15,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:16,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:16,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:18,574][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:17:19,534][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:17:19,536][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:17:21,028][__main__][INFO] - Iteration 5 took 55s (37.99% Gen, 62.00% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 17m 33s. Estimated total time: 15h 24m 23s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 26s, 500 more iterations: 7h 42m 11s. +[2025-08-20 08:17:21,030][__main__][INFO] - Starting iteration 5. +[2025-08-20 08:17:44,282][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:17:44,283][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:17:44,290][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:17:46,726][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:17:46,727][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:17:46,733][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:17:46,736][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:17:46,736][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:17:47,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:47,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:48,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:49,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:50,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:50,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:51,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:52,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:53,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:54,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:54,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:55,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:56,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:57,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:58,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:58,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:17:59,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:00,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:01,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:02,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:03,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:04,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:04,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:05,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:06,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:07,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:08,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:08,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:09,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:10,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:11,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:12,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:13,645][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:18:14,726][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:18:14,729][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:18:16,225][__main__][INFO] - Iteration 6 took 55s (37.68% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 12m 9s. Estimated total time: 15h 19m 54s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 59s, 500 more iterations: 7h 39m 57s. +[2025-08-20 08:18:16,226][__main__][INFO] - Starting iteration 6. +[2025-08-20 08:18:40,356][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:18:40,358][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:18:40,364][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:18:42,809][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:18:42,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:18:42,817][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:18:42,819][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:18:42,820][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:18:43,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:43,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:44,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:45,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:46,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:47,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:47,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:48,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:49,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:50,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:51,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:51,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:52,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:53,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:54,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:55,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:55,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:56,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:57,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:58,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:18:58,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:00,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:01,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:01,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:02,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:03,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:04,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:05,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:05,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:06,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:07,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:08,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:09,798][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:19:10,714][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:19:10,716][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:19:12,110][__main__][INFO] - Iteration 7 took 55s (38.82% Gen, 61.18% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 22m 42s. Estimated total time: 15h 31m 23s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 8s, 500 more iterations: 7h 45m 41s. +[2025-08-20 08:19:12,112][__main__][INFO] - Starting iteration 7. +[2025-08-20 08:19:35,427][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:19:35,428][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:19:35,435][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:19:37,891][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:19:37,892][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:19:37,899][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:19:37,901][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:19:37,901][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:19:38,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:38,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:39,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:40,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:41,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:42,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:42,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:43,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:44,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:45,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:46,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:46,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:47,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:48,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:49,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:50,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:50,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:51,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:52,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:53,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:54,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:54,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:55,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:56,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:57,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:58,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:19:59,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:00,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:01,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:01,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:02,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:03,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:05,031][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:20:06,043][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:20:06,044][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:20:07,894][__main__][INFO] - Iteration 8 took 55s (37.41% Gen, 62.58% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 20m 5s. Estimated total time: 15h 29m 41s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 58s, 500 more iterations: 7h 44m 50s. +[2025-08-20 08:20:07,896][__main__][INFO] - Starting iteration 8. +[2025-08-20 08:20:31,075][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:20:31,076][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:20:31,082][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:20:33,526][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:20:33,527][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:20:33,534][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:20:33,537][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:20:33,537][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:20:33,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:34,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:35,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:36,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:37,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:37,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:38,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:39,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:40,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:40,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:41,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:42,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:43,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:44,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:44,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:45,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:46,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:47,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:48,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:49,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:50,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:50,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:51,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:52,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:53,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:54,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:54,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:55,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:56,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:57,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:58,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:20:58,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:00,564][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:21:01,651][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:21:01,654][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:21:03,725][__main__][INFO] - Iteration 9 took 55s (37.14% Gen, 62.86% Train). Generation: 20s, Training: 35s. Estimated remaining time: 15h 19m 56s. Estimated total time: 15h 30m 29s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 2s, 500 more iterations: 7h 45m 14s. +[2025-08-20 08:21:03,823][__main__][INFO] - Starting iteration 9. +[2025-08-20 08:21:27,410][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:21:27,412][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:21:27,418][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:21:29,883][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:21:29,885][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:21:29,891][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:21:29,893][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:21:29,894][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:21:30,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:30,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:31,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:32,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:33,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:34,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:34,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:35,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:36,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:37,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:38,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:38,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:39,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:40,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:41,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:42,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:42,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:43,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:44,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:45,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:46,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:47,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:48,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:48,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:49,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:50,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:51,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:52,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:52,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:53,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:54,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:55,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:21:56,878][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:21:57,826][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:21:57,828][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:21:59,176][__main__][INFO] - Iteration 10 took 55s (38.15% Gen, 61.84% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 11m 3s. Estimated total time: 15h 22m 31s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 15s. +[2025-08-20 08:21:59,708][__main__][INFO] - Starting iteration 10. +[2025-08-20 08:22:22,777][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:22:22,779][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:22:22,785][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:22:25,218][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:22:25,219][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:22:25,226][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:22:25,228][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:22:25,228][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:22:25,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:26,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:27,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:27,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:28,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:29,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:30,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:31,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:31,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:32,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:33,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:34,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:35,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:35,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:36,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:37,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:38,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:39,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:39,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:40,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:41,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:42,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:43,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:44,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:45,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:45,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:46,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:47,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:48,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:49,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:49,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:50,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:22:52,255][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:22:53,204][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:22:53,206][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:22:54,684][__main__][INFO] - Iteration 11 took 54s (37.51% Gen, 62.49% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 3m 51s. Estimated total time: 15h 16m 15s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 7s. +[2025-08-20 08:22:54,686][__main__][INFO] - Starting iteration 11. +[2025-08-20 08:23:18,313][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:23:18,314][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:23:18,320][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:23:20,756][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:23:20,757][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:23:20,763][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:23:20,766][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:23:20,766][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:23:21,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:21,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:22,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:23,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:24,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:25,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:25,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:26,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:27,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:28,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:29,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:29,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:30,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:31,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:32,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:32,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:33,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:34,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:35,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:36,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:36,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:38,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:39,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:39,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:40,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:41,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:42,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:43,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:43,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:44,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:45,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:46,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:23:47,798][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:23:48,744][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:23:48,746][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:23:50,100][__main__][INFO] - Iteration 12 took 55s (38.25% Gen, 61.75% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 10m 14s. Estimated total time: 15h 23m 34s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 21s, 500 more iterations: 7h 41m 47s. +[2025-08-20 08:23:50,102][__main__][INFO] - Starting iteration 12. +[2025-08-20 08:24:13,778][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:24:13,779][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:24:13,785][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:24:16,246][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:24:16,248][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:24:16,254][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:24:16,256][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:24:16,257][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:24:16,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:17,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:18,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:18,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:19,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:20,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:21,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:22,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:22,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:23,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:24,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:25,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:26,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:26,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:27,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:28,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:29,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:30,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:30,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:31,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:32,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:33,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:34,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:34,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:35,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:36,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:37,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:38,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:39,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:40,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:41,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:42,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:24:43,653][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:24:44,617][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:24:44,619][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:24:45,996][__main__][INFO] - Iteration 13 took 55s (37.97% Gen, 62.03% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 17m 19s. Estimated total time: 15h 31m 34s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 9s, 500 more iterations: 7h 45m 47s. +[2025-08-20 08:24:45,998][__main__][INFO] - Starting iteration 13. +[2025-08-20 08:25:09,157][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:25:09,158][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:25:09,164][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:25:11,630][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:25:11,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:25:11,638][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:25:11,640][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:25:11,641][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:25:11,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:12,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:13,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:14,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:15,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:15,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:16,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:17,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:18,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:19,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:19,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:20,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:21,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:22,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:23,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:23,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:24,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:25,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:26,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:27,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:27,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:28,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:29,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:30,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:31,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:32,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:33,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:33,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:34,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:35,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:36,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:37,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:25:38,677][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:25:39,672][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:25:39,674][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:25:41,017][__main__][INFO] - Iteration 14 took 55s (37.63% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 1m 48s. Estimated total time: 15h 16m 58s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 29s. +[2025-08-20 08:25:41,018][__main__][INFO] - Starting iteration 14. +[2025-08-20 08:26:04,198][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:04,200][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:04,206][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:06,669][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:06,670][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:06,676][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:06,679][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:26:06,679][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:26:06,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:07,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:08,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:09,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:10,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:10,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:11,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:12,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:13,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:14,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:14,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:15,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:16,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:17,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:18,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:19,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:20,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:20,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:21,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:22,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:23,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:24,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:24,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:25,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:26,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:27,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:28,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:28,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:29,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:30,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:31,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:32,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:26:33,693][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:26:34,654][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:26:34,655][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:26:35,919][__main__][INFO] - Iteration 15 took 54s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 58m 55s. Estimated total time: 15h 15m 0s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 30s. +[2025-08-20 08:26:35,921][__main__][INFO] - Starting iteration 15. +[2025-08-20 08:26:59,445][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:59,447][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:26:59,453][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:01,931][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:01,932][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:01,939][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:01,941][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:27:01,941][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:27:02,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:03,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:03,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:04,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:05,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:06,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:06,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:07,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:08,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:09,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:10,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:10,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:11,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:12,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:13,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:14,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:14,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:15,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:16,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:17,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:18,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:18,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:19,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:20,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:21,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:22,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:23,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:24,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:24,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:25,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:26,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:27,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:28,964][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:27:29,933][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:27:29,935][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:27:31,254][__main__][INFO] - Iteration 16 took 55s (38.03% Gen, 61.97% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 5m 12s. Estimated total time: 15h 22m 12s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 6s. +[2025-08-20 08:27:31,256][__main__][INFO] - Starting iteration 16. +[2025-08-20 08:27:54,647][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:54,649][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:54,655][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:57,125][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:57,126][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:57,134][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:27:57,136][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:27:57,137][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:27:57,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:58,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:59,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:27:59,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:00,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:01,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:02,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:02,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:03,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:04,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:05,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:06,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:06,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:07,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:08,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:09,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:10,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:10,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:11,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:12,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:13,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:14,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:15,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:16,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:16,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:17,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:18,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:19,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:20,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:20,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:21,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:22,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:24,109][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:28:25,056][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:28:25,058][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:28:28,135][__main__][INFO] - Iteration 17 took 56s (36.80% Gen, 63.19% Train). Generation: 20s, Training: 35s. Estimated remaining time: 15h 30m 1s. Estimated total time: 15h 47m 58s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 47s, 500 more iterations: 7h 53m 59s. +[2025-08-20 08:28:28,136][__main__][INFO] - Starting iteration 17. +[2025-08-20 08:28:51,236][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:28:51,237][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:28:51,244][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:28:53,709][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:28:53,711][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:28:53,717][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:28:53,719][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:28:53,720][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:28:54,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:54,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:55,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:56,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:57,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:57,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:58,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:28:59,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:00,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:01,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:01,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:02,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:03,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:04,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:05,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:05,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:06,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:07,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:08,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:09,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:09,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:10,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:11,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:12,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:13,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:14,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:15,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:15,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:16,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:17,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:18,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:19,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:20,759][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:29:21,703][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:29:21,704][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:29:23,122][__main__][INFO] - Iteration 18 took 54s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 57m 32s. Estimated total time: 15h 16m 24s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 12s. +[2025-08-20 08:29:23,123][__main__][INFO] - Starting iteration 18. +[2025-08-20 08:29:46,273][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:29:46,274][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:29:46,280][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:29:48,747][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:29:48,749][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:29:48,755][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:29:48,758][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:29:48,758][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:29:49,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:49,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:50,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:51,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:52,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:53,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:53,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:54,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:55,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:56,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:56,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:57,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:58,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:29:59,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:00,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:00,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:01,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:02,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:03,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:04,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:05,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:05,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:07,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:08,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:09,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:09,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:10,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:11,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:12,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:13,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:13,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:14,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:16,347][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:30:17,301][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:30:17,302][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:30:18,666][__main__][INFO] - Iteration 19 took 55s (37.28% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 15h 5m 54s. Estimated total time: 15h 25m 41s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 34s, 500 more iterations: 7h 42m 50s. +[2025-08-20 08:30:18,668][__main__][INFO] - Starting iteration 19. +[2025-08-20 08:30:42,159][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:30:42,160][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:30:42,166][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:30:44,606][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:30:44,607][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:30:44,614][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:30:44,616][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:30:44,617][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:30:44,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:45,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:46,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:47,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:48,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:48,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:49,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:50,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:51,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:52,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:52,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:53,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:54,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:55,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:56,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:56,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:57,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:58,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:30:59,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:00,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:01,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:02,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:02,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:03,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:04,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:05,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:06,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:06,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:07,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:08,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:09,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:10,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:11,639][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:31:12,579][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:31:12,581][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:31:13,955][__main__][INFO] - Iteration 20 took 55s (38.08% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 0m 44s. Estimated total time: 15h 21m 26s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 8s, 500 more iterations: 7h 40m 43s. +[2025-08-20 08:31:13,957][__main__][INFO] - Starting iteration 20. +[2025-08-20 08:31:40,605][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:31:40,606][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:31:40,613][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:31:43,087][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:31:43,088][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:31:43,095][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:31:43,097][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:31:43,097][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:31:43,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:44,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:44,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:45,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:46,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:47,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:48,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:48,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:49,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:50,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:51,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:52,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:52,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:53,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:54,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:55,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:56,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:56,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:57,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:58,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:31:59,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:00,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:01,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:02,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:02,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:03,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:04,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:05,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:06,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:06,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:07,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:08,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:10,062][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:32:10,996][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:32:10,997][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:32:12,365][__main__][INFO] - Iteration 21 took 58s (41.38% Gen, 58.62% Train). Generation: 24s, Training: 34s. Estimated remaining time: 15h 51m 46s. Estimated total time: 16h 13m 27s. Time estimates for 10 more iterations: 9m 44s, 100 more iterations: 1h 37m 20s, 500 more iterations: 8h 6m 43s. +[2025-08-20 08:32:12,366][__main__][INFO] - Starting iteration 21. +[2025-08-20 08:32:35,524][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:32:35,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:32:35,531][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:32:37,961][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:32:37,963][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:32:37,969][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:32:37,971][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:32:37,971][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:32:38,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:39,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:39,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:40,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:41,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:42,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:43,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:43,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:44,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:45,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:46,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:47,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:47,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:48,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:49,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:50,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:51,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:52,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:52,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:53,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:54,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:55,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:56,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:57,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:58,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:59,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:32:59,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:00,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:01,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:02,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:03,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:03,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:05,427][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:33:06,373][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:33:06,375][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:33:07,701][__main__][INFO] - Iteration 22 took 55s (37.47% Gen, 62.53% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 59m 37s. Estimated total time: 15h 22m 13s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 6s. +[2025-08-20 08:33:07,702][__main__][INFO] - Starting iteration 22. +[2025-08-20 08:33:30,823][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:33:30,825][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:33:30,831][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:33:33,284][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:33:33,285][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:33:33,292][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:33:33,294][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:33:33,295][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:33:33,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:34,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:35,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:35,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:36,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:37,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:38,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:39,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:39,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:40,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:41,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:42,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:43,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:43,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:44,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:45,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:46,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:47,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:47,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:48,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:49,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:50,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:51,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:52,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:53,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:53,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:54,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:55,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:56,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:57,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:57,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:33:58,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:00,328][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:34:01,354][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:34:01,356][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:34:02,824][__main__][INFO] - Iteration 23 took 55s (37.51% Gen, 62.49% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 55m 9s. Estimated total time: 15h 18m 41s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 20s. +[2025-08-20 08:34:02,825][__main__][INFO] - Starting iteration 23. +[2025-08-20 08:34:26,000][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:34:26,001][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:34:26,008][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:34:28,449][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:34:28,450][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:34:28,457][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:34:28,459][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:34:28,460][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:34:28,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:29,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:30,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:31,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:31,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:32,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:33,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:34,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:35,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:35,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:36,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:37,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:38,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:39,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:39,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:40,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:41,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:42,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:43,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:43,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:44,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:45,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:46,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:47,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:48,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:49,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:49,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:50,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:51,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:52,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:53,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:53,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:34:55,453][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:34:56,374][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:34:56,375][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:34:57,723][__main__][INFO] - Iteration 24 took 54s (37.78% Gen, 62.22% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 50m 30s. Estimated total time: 15h 14m 57s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 28s. +[2025-08-20 08:34:57,725][__main__][INFO] - Starting iteration 24. +[2025-08-20 08:35:21,677][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:35:21,679][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:35:21,685][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:35:24,159][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:35:24,160][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:35:24,167][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:35:24,169][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:35:24,169][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:35:24,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:25,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:26,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:26,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:27,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:28,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:29,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:30,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:30,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:31,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:32,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:33,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:33,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:34,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:35,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:36,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:37,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:38,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:39,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:40,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:40,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:41,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:42,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:43,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:43,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:44,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:45,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:46,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:47,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:47,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:48,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:49,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:35:51,120][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:35:52,029][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:35:52,031][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:35:53,280][__main__][INFO] - Iteration 25 took 55s (38.69% Gen, 61.31% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 0m 32s. Estimated total time: 15h 25m 55s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 35s, 500 more iterations: 7h 42m 57s. +[2025-08-20 08:35:53,282][__main__][INFO] - Starting iteration 25. +[2025-08-20 08:36:16,833][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:36:16,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:36:16,841][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:36:19,302][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:36:19,303][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:36:19,310][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:36:19,312][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:36:19,313][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:36:19,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:20,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:21,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:21,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:22,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:23,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:24,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:25,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:25,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:26,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:27,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:28,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:29,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:29,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:30,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:31,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:32,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:33,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:33,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:34,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:35,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:36,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:37,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:38,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:39,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:39,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:40,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:41,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:42,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:43,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:43,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:44,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:36:46,317][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:36:47,426][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:36:47,428][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:36:48,720][__main__][INFO] - Iteration 26 took 55s (38.08% Gen, 61.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 57m 39s. Estimated total time: 15h 23m 57s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 23s, 500 more iterations: 7h 41m 58s. +[2025-08-20 08:36:48,721][__main__][INFO] - Starting iteration 26. +[2025-08-20 08:37:11,812][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:37:11,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:37:11,820][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:37:14,267][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:37:14,269][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:37:14,275][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:37:14,278][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:37:14,278][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:37:14,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:15,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:16,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:16,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:17,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:18,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:19,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:20,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:20,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:21,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:22,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:23,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:24,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:24,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:25,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:26,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:27,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:28,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:28,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:29,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:30,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:31,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:32,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:33,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:34,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:34,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:35,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:36,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:37,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:38,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:38,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:39,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:37:41,214][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:37:42,133][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:37:42,135][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:37:43,549][__main__][INFO] - Iteration 27 took 54s (37.65% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 46m 35s. Estimated total time: 15h 13m 47s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 53s. +[2025-08-20 08:37:43,551][__main__][INFO] - Starting iteration 27. +[2025-08-20 08:38:07,630][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:38:07,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:38:07,638][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:38:10,086][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:38:10,087][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:38:10,094][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:38:10,096][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:38:10,097][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:38:10,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:11,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:11,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:12,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:13,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:14,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:15,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:15,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:16,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:17,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:18,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:19,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:19,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:20,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:21,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:22,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:23,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:23,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:24,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:25,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:26,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:27,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:27,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:28,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:29,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:30,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:31,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:31,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:33,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:33,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:34,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:35,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:38:37,083][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:38:38,023][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:38:38,024][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:38:39,312][__main__][INFO] - Iteration 28 took 55s (38.77% Gen, 61.23% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 1m 12s. Estimated total time: 15h 29m 20s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 56s, 500 more iterations: 7h 44m 40s. +[2025-08-20 08:38:39,313][__main__][INFO] - Starting iteration 28. +[2025-08-20 08:39:02,669][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:02,671][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:02,677][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:05,149][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:05,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:05,157][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:05,159][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:39:05,160][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:39:05,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:06,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:07,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:07,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:08,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:09,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:10,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:11,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:11,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:12,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:13,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:14,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:14,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:15,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:16,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:17,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:18,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:18,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:19,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:20,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:21,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:22,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:22,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:23,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:25,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:25,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:26,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:27,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:28,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:29,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:29,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:30,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:39:32,159][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:39:33,091][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:39:33,092][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:39:34,458][__main__][INFO] - Iteration 29 took 55s (37.87% Gen, 62.12% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 50m 0s. Estimated total time: 15h 19m 3s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 31s. +[2025-08-20 08:39:34,459][__main__][INFO] - Starting iteration 29. +[2025-08-20 08:39:57,695][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:57,697][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:39:57,703][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:00,160][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:00,162][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:00,168][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:00,170][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:40:00,171][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:40:00,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:01,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:02,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:02,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:03,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:04,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:05,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:06,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:06,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:07,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:08,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:09,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:10,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:10,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:11,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:12,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:13,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:13,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:15,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:16,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:16,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:17,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:18,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:19,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:19,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:20,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:21,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:22,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:23,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:23,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:24,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:25,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:27,120][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:40:28,207][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:40:28,209][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:40:29,531][__main__][INFO] - Iteration 30 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 47m 53s. Estimated total time: 15h 17m 51s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 55s. +[2025-08-20 08:40:29,533][__main__][INFO] - Starting iteration 30. +[2025-08-20 08:40:53,072][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:53,074][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:53,080][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:55,524][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:55,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:55,532][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:40:55,534][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:40:55,535][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:40:55,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:56,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:57,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:58,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:59,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:40:59,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:00,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:01,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:02,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:02,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:03,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:04,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:05,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:06,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:06,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:07,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:08,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:09,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:10,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:10,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:11,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:12,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:13,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:14,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:14,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:16,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:16,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:17,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:18,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:19,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:20,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:20,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:22,513][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:41:23,559][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:41:23,561][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:41:24,844][__main__][INFO] - Iteration 31 took 55s (38.12% Gen, 61.88% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 50m 56s. Estimated total time: 15h 21m 50s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 55s. +[2025-08-20 08:41:24,846][__main__][INFO] - Starting iteration 31. +[2025-08-20 08:41:47,997][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:41:47,999][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:41:48,005][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:41:50,458][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:41:50,459][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:41:50,466][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:41:50,469][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:41:50,469][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:41:50,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:51,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:52,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:53,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:53,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:54,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:55,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:56,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:57,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:57,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:58,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:41:59,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:00,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:01,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:01,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:02,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:03,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:04,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:05,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:05,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:06,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:07,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:08,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:09,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:10,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:11,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:11,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:12,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:13,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:14,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:15,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:15,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:17,512][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:42:18,464][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:42:18,465][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:42:19,725][__main__][INFO] - Iteration 32 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 42m 49s. Estimated total time: 15h 14m 38s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 19s. +[2025-08-20 08:42:19,726][__main__][INFO] - Starting iteration 32. +[2025-08-20 08:42:42,888][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:42:42,890][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:42:42,896][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:42:45,354][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:42:45,355][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:42:45,362][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:42:45,364][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:42:45,365][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:42:45,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:46,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:47,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:48,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:48,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:49,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:50,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:51,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:52,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:52,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:53,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:54,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:55,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:55,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:56,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:57,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:58,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:59,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:42:59,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:00,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:01,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:02,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:03,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:04,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:05,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:05,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:06,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:07,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:08,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:09,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:09,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:10,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:12,270][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:43:13,262][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:43:13,264][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:43:15,676][__main__][INFO] - Iteration 33 took 55s (37.02% Gen, 62.98% Train). Generation: 20s, Training: 35s. Estimated remaining time: 14h 59m 44s. Estimated total time: 15h 32m 29s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 14s, 500 more iterations: 7h 46m 14s. +[2025-08-20 08:43:15,678][__main__][INFO] - Starting iteration 33. +[2025-08-20 08:43:38,899][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:43:38,900][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:43:38,907][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:43:41,368][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:43:41,369][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:43:41,375][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:43:41,378][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:43:41,378][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:43:41,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:42,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:43,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:44,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:44,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:45,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:46,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:47,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:48,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:48,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:49,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:50,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:51,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:52,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:52,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:53,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:54,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:55,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:55,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:56,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:57,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:58,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:59,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:43:59,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:00,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:01,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:02,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:03,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:04,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:05,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:05,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:06,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:08,337][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:44:09,285][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:44:09,286][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:44:10,578][__main__][INFO] - Iteration 34 took 54s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 41m 20s. Estimated total time: 15h 14m 59s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 29s. +[2025-08-20 08:44:10,580][__main__][INFO] - Starting iteration 34. +[2025-08-20 08:44:33,691][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:44:33,693][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:44:33,699][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:44:36,149][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:44:36,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:44:36,157][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:44:36,159][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:44:36,160][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:44:36,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:37,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:38,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:38,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:39,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:40,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:41,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:42,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:42,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:43,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:44,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:45,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:45,991][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:54,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:55,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:55,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:56,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:57,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:58,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:59,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:44:59,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:00,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:01,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:03,081][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:45:04,026][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:45:04,028][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:45:05,500][__main__][INFO] - Iteration 35 took 54s (37.63% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 40m 45s. Estimated total time: 15h 15m 19s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 39s. +[2025-08-20 08:45:05,501][__main__][INFO] - Starting iteration 35. +[2025-08-20 08:45:29,052][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:45:29,053][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:45:29,060][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:45:31,528][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:45:31,529][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:45:31,535][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:45:31,537][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:45:31,539][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:45:31,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:32,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:33,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:34,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:35,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:35,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:36,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:37,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:38,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:38,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:39,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:40,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:41,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:42,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:42,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:43,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:44,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:45,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:46,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:46,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:47,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:48,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:49,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:50,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:51,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:52,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:52,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:53,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:54,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:55,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:56,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:56,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:45:58,452][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:45:59,437][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:45:59,438][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:46:00,821][__main__][INFO] - Iteration 36 took 55s (38.14% Gen, 61.86% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 46m 28s. Estimated total time: 15h 21m 58s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 59s. +[2025-08-20 08:46:00,823][__main__][INFO] - Starting iteration 36. +[2025-08-20 08:46:24,801][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:46:24,802][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:46:24,808][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:46:27,277][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:46:27,278][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:46:27,285][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:46:27,288][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:46:27,288][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:46:27,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:28,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:29,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:29,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:30,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:31,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:32,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:33,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:33,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:34,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:35,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:36,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:37,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:37,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:38,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:39,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:40,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:41,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:41,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:42,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:43,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:44,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:45,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:46,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:47,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:47,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:48,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:49,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:50,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:51,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:51,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:52,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:46:54,260][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:46:55,375][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:46:55,378][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:46:58,459][__main__][INFO] - Iteration 37 took 57s (37.32% Gen, 62.68% Train). Generation: 21s, Training: 36s. Estimated remaining time: 15h 24m 8s. Estimated total time: 16h 0m 35s. Time estimates for 10 more iterations: 9m 36s, 100 more iterations: 1h 36m 3s, 500 more iterations: 8h 0m 17s. +[2025-08-20 08:46:58,460][__main__][INFO] - Starting iteration 37. +[2025-08-20 08:47:21,685][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:47:21,687][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:47:21,693][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:47:24,152][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:47:24,154][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:47:24,160][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:47:24,163][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:47:24,163][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:47:24,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:25,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:26,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:26,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:27,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:28,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:29,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:30,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:30,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:31,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:32,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:33,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:33,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:34,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:35,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:36,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:37,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:37,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:38,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:39,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:40,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:41,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:42,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:43,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:43,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:44,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:45,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:46,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:47,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:47,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:48,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:49,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:47:51,074][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:47:52,019][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:47:52,020][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:47:54,809][__main__][INFO] - Iteration 38 took 56s (36.87% Gen, 63.13% Train). Generation: 20s, Training: 35s. Estimated remaining time: 15h 1m 44s. Estimated total time: 15h 39m 8s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 54s, 500 more iterations: 7h 49m 34s. +[2025-08-20 08:47:54,811][__main__][INFO] - Starting iteration 38. +[2025-08-20 08:48:18,259][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:48:18,261][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:48:18,267][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:48:20,723][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:48:20,725][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:48:20,731][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:48:20,733][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:48:20,734][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:48:21,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:21,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:22,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:23,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:24,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:24,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:25,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:26,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:27,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:28,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:28,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:29,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:30,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:31,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:32,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:32,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:33,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:34,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:35,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:36,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:37,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:38,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:38,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:39,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:40,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:41,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:42,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:42,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:43,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:44,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:45,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:46,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:48:47,621][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:48:48,639][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:48:48,641][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:48:50,163][__main__][INFO] - Iteration 39 took 55s (37.93% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 44m 12s. Estimated total time: 15h 22m 31s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 15s. +[2025-08-20 08:48:50,164][__main__][INFO] - Starting iteration 39. +[2025-08-20 08:49:13,362][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:49:13,363][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:49:13,369][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:49:15,836][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:49:15,837][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:49:15,845][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:49:15,848][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:49:15,848][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:49:16,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:16,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:17,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:18,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:19,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:20,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:20,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:21,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:22,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:23,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:24,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:24,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:25,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:26,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:27,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:28,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:28,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:29,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:30,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:31,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:32,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:33,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:34,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:34,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:35,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:36,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:37,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:38,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:38,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:39,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:40,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:41,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:49:42,821][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:49:43,811][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:49:43,813][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:49:45,250][__main__][INFO] - Iteration 40 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 38m 51s. Estimated total time: 15h 18m 5s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 2s. +[2025-08-20 08:49:45,252][__main__][INFO] - Starting iteration 40. +[2025-08-20 08:50:08,844][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:50:08,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:50:08,851][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:50:11,318][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:50:11,319][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:50:11,326][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:50:11,328][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:50:11,329][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:50:11,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:12,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:13,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:14,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:14,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:15,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:16,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:17,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:17,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:18,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:19,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:20,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:21,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:21,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:22,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:23,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:24,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:25,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:26,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:27,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:27,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:28,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:29,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:30,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:31,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:31,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:33,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:33,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:34,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:35,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:36,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:37,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:50:38,558][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:50:39,525][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:50:39,530][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:50:40,978][__main__][INFO] - Iteration 41 took 55s (37.94% Gen, 62.06% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 48m 35s. Estimated total time: 15h 28m 45s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 52s, 500 more iterations: 7h 44m 22s. +[2025-08-20 08:50:40,979][__main__][INFO] - Starting iteration 41. +[2025-08-20 08:51:04,320][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:51:04,321][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:51:04,327][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:51:06,805][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:51:06,807][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:51:06,813][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:51:06,816][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:51:06,816][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:51:07,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:07,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:08,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:09,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:10,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:11,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:11,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:12,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:13,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:14,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:15,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:15,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:16,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:17,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:18,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:19,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:19,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:21,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:21,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:22,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:23,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:24,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:25,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:25,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:26,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:27,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:28,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:29,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:29,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:30,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:31,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:32,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:51:33,781][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:51:34,759][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:51:34,760][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:51:36,096][__main__][INFO] - Iteration 42 took 55s (37.81% Gen, 62.18% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 37m 31s. Estimated total time: 15h 18m 36s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 51s, 500 more iterations: 7h 39m 18s. +[2025-08-20 08:51:36,098][__main__][INFO] - Starting iteration 42. +[2025-08-20 08:52:00,432][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:00,433][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:00,440][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:02,919][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:02,920][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:02,927][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:02,929][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:52:02,929][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:52:03,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:04,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:04,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:05,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:06,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:07,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:07,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:08,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:09,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:10,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:11,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:11,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:12,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:13,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:14,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:15,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:15,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:16,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:17,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:18,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:19,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:19,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:20,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:21,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:22,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:23,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:24,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:25,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:26,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:26,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:27,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:28,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:29,979][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:52:30,979][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:52:30,982][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:52:32,290][__main__][INFO] - Iteration 43 took 56s (38.87% Gen, 61.13% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 54m 30s. Estimated total time: 15h 36m 31s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 39s, 500 more iterations: 7h 48m 15s. +[2025-08-20 08:52:32,292][__main__][INFO] - Starting iteration 43. +[2025-08-20 08:52:55,870][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:55,871][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:55,877][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:58,347][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:58,348][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:58,355][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:52:58,357][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:52:58,358][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:52:58,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:52:59,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:00,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:01,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:01,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:02,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:03,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:04,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:05,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:05,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:06,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:07,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:08,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:08,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:09,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:10,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:11,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:12,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:12,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:13,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:14,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:15,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:16,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:17,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:18,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:18,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:19,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:20,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:21,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:22,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:22,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:23,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:25,321][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:53:26,518][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:53:26,521][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:53:27,886][__main__][INFO] - Iteration 44 took 55s (37.96% Gen, 62.04% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 43m 36s. Estimated total time: 15h 26m 33s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 39s, 500 more iterations: 7h 43m 16s. +[2025-08-20 08:53:27,888][__main__][INFO] - Starting iteration 44. +[2025-08-20 08:53:51,253][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:53:51,255][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:53:51,261][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:53:53,704][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:53:53,705][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:53:53,712][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:53:53,714][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:53:53,714][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:53:54,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:54,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:55,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:56,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:57,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:57,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:58,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:53:59,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:00,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:01,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:01,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:02,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:03,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:04,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:05,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:05,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:06,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:07,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:08,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:09,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:09,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:10,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:11,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:12,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:13,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:14,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:15,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:15,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:16,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:17,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:18,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:19,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:20,647][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:54:21,607][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:54:21,608][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:54:23,082][__main__][INFO] - Iteration 45 took 55s (37.88% Gen, 62.12% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 36m 2s. Estimated total time: 15h 19m 54s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 59s, 500 more iterations: 7h 39m 57s. +[2025-08-20 08:54:23,084][__main__][INFO] - Starting iteration 45. +[2025-08-20 08:54:46,763][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:54:46,764][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:54:46,770][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:54:49,237][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:54:49,239][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:54:49,246][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:54:49,249][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:54:49,249][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:54:49,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:50,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:51,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:51,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:52,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:53,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:54,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:55,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:55,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:56,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:57,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:58,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:59,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:54:59,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:00,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:01,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:02,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:03,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:04,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:05,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:05,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:06,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:07,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:08,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:09,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:10,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:11,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:11,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:12,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:13,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:14,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:15,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:16,678][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:55:17,876][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:55:17,878][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:55:19,243][__main__][INFO] - Iteration 46 took 56s (37.77% Gen, 62.23% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 51m 10s. Estimated total time: 15h 35m 59s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 35s, 500 more iterations: 7h 47m 59s. +[2025-08-20 08:55:19,246][__main__][INFO] - Starting iteration 46. +[2025-08-20 08:55:42,803][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:55:42,804][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:55:42,810][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:55:45,261][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:55:45,263][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:55:45,269][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:55:45,272][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:55:45,272][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:55:45,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:46,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:47,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:47,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:48,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:49,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:50,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:51,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:51,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:52,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:53,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:54,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:55,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:55,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:56,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:57,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:58,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:59,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:55:59,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:00,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:01,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:02,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:03,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:04,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:05,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:05,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:06,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:07,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:08,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:09,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:09,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:10,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:12,274][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:56:13,247][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:56:13,249][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:56:14,601][__main__][INFO] - Iteration 47 took 55s (38.13% Gen, 61.87% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 36m 51s. Estimated total time: 15h 22m 35s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 17s. +[2025-08-20 08:56:14,603][__main__][INFO] - Starting iteration 47. +[2025-08-20 08:56:38,329][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:56:38,330][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:56:38,337][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:56:40,809][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:56:40,810][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:56:40,816][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:56:40,819][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:56:40,819][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:56:41,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:41,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:42,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:43,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:44,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:45,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:45,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:46,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:47,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:48,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:49,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:49,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:50,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:51,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:52,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:53,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:53,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:54,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:55,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:56,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:57,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:58,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:59,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:56:59,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:00,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:01,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:02,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:03,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:03,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:04,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:05,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:06,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:07,764][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:57:08,720][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:57:08,721][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:57:09,959][__main__][INFO] - Iteration 48 took 55s (38.43% Gen, 61.57% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 35m 57s. Estimated total time: 15h 22m 36s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 18s. +[2025-08-20 08:57:09,961][__main__][INFO] - Starting iteration 48. +[2025-08-20 08:57:34,428][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:57:34,429][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:57:34,436][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:57:36,881][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:57:36,882][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:57:36,889][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:57:36,891][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:57:36,891][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:57:37,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:37,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:38,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:39,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:40,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:41,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:41,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:42,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:43,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:44,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:45,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:46,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:47,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:48,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:49,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:49,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:50,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:51,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:52,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:53,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:53,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:54,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:55,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:56,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:57,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:58,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:59,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:57:59,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:00,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:01,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:02,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:03,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:04,560][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:58:05,512][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:58:05,513][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:58:06,856][__main__][INFO] - Iteration 49 took 56s (38.69% Gen, 61.31% Train). Generation: 22s, Training: 34s. Estimated remaining time: 15h 0m 38s. Estimated total time: 15h 48m 14s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 49s, 500 more iterations: 7h 54m 7s. +[2025-08-20 08:58:06,857][__main__][INFO] - Starting iteration 49. +[2025-08-20 08:58:31,730][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:58:31,732][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:58:31,738][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:58:34,199][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:58:34,200][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:58:34,207][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:58:34,209][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:58:34,210][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:58:34,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:35,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:36,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:36,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:37,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:38,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:39,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:40,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:40,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:41,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:42,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:43,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:44,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:44,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:45,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:46,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:47,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:48,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:48,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:49,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:50,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:51,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:51,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:52,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:53,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:54,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:55,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:56,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:57,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:57,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:58,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:58:59,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:01,131][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:59:02,106][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:59:02,107][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 08:59:03,460][__main__][INFO] - Iteration 50 took 56s (39.59% Gen, 60.41% Train). Generation: 22s, Training: 34s. Estimated remaining time: 14h 54m 49s. Estimated total time: 15h 43m 22s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 20s, 500 more iterations: 7h 51m 41s. +[2025-08-20 08:59:03,462][__main__][INFO] - Starting iteration 50. +[2025-08-20 08:59:27,816][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:59:27,818][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:59:27,824][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:59:30,323][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:59:30,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:59:30,330][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 08:59:30,333][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 08:59:30,333][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 08:59:30,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:31,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:32,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:33,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:33,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:34,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:35,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:36,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:36,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:37,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:38,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:39,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:40,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:40,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:41,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:42,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:43,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:44,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:44,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:45,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:46,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:47,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:48,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:48,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:50,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:50,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:51,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:52,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:53,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:54,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:54,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:55,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 08:59:57,256][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 08:59:58,240][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 08:59:58,241][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:00:02,757][__main__][INFO] - Iteration 51 took 59s (36.87% Gen, 58.90% Train). Generation: 21s, Training: 34s. Estimated remaining time: 15h 38m 43s. Estimated total time: 16h 28m 15s. Time estimates for 10 more iterations: 9m 52s, 100 more iterations: 1h 38m 49s, 500 more iterations: 8h 14m 7s. +[2025-08-20 09:00:02,759][__main__][INFO] - Starting iteration 51. +[2025-08-20 09:00:26,816][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:00:26,817][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:00:26,824][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:00:29,285][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:00:29,287][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:00:29,293][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:00:29,296][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:00:29,296][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:00:29,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:30,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:31,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:31,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:32,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:33,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:34,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:35,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:35,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:36,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:37,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:38,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:39,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:39,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:40,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:41,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:42,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:43,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:44,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:45,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:45,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:46,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:47,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:48,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:49,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:49,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:50,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:51,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:52,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:53,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:54,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:54,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:00:56,536][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:00:57,492][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:00:57,493][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:00:58,808][__main__][INFO] - Iteration 52 took 56s (38.56% Gen, 61.43% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 43m 40s. Estimated total time: 15h 34m 8s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 24s, 500 more iterations: 7h 47m 4s. +[2025-08-20 09:00:58,809][__main__][INFO] - Starting iteration 52. +[2025-08-20 09:01:26,815][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:01:26,816][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:01:26,822][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:01:29,274][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:01:29,275][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:01:29,281][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:01:29,283][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:01:29,284][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:01:29,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:30,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:31,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:31,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:32,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:33,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:34,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:35,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:35,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:36,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:37,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:38,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:39,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:39,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:40,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:41,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:42,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:43,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:43,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:44,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:45,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:46,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:47,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:48,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:49,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:49,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:50,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:51,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:52,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:53,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:53,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:54,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:01:56,162][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:01:59,527][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:01:59,532][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:02:00,793][__main__][INFO] - Iteration 53 took 1m 1s (41.19% Gen, 58.81% Train). Generation: 25s, Training: 36s. Estimated remaining time: 16h 21m 34s. Estimated total time: 17h 13m 3s. Time estimates for 10 more iterations: 10m 19s, 100 more iterations: 1h 43m 18s, 500 more iterations: 8h 36m 31s. +[2025-08-20 09:02:00,795][__main__][INFO] - Starting iteration 53. +[2025-08-20 09:02:24,238][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:02:24,240][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:02:24,246][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:02:26,710][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:02:26,711][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:02:26,717][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:02:26,719][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:02:26,720][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:02:27,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:27,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:28,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:29,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:30,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:30,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:31,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:32,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:33,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:34,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:34,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:35,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:36,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:37,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:38,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:38,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:39,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:40,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:41,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:42,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:42,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:43,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:44,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:45,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:46,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:46,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:48,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:48,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:49,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:50,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:51,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:52,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:02:53,640][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:02:54,631][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:02:54,633][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:02:55,955][__main__][INFO] - Iteration 54 took 55s (38.03% Gen, 61.96% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 26m 54s. Estimated total time: 15h 19m 19s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 39s. +[2025-08-20 09:02:55,957][__main__][INFO] - Starting iteration 54. +[2025-08-20 09:03:19,342][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:03:19,343][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:03:19,349][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:03:21,803][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:03:21,805][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:03:21,811][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:03:21,813][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:03:21,814][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:03:22,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:22,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:23,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:24,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:25,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:26,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:26,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:27,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:28,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:29,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:30,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:30,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:31,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:32,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:33,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:34,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:34,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:35,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:36,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:37,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:37,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:39,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:40,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:40,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:41,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:42,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:43,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:43,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:44,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:45,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:46,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:47,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:03:48,743][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:03:49,699][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:03:49,701][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:03:51,070][__main__][INFO] - Iteration 55 took 55s (38.00% Gen, 62.00% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 25m 12s. Estimated total time: 15h 18m 32s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 51s, 500 more iterations: 7h 39m 16s. +[2025-08-20 09:03:51,071][__main__][INFO] - Starting iteration 55. +[2025-08-20 09:04:14,836][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:04:14,838][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:04:14,844][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:04:17,300][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:04:17,301][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:04:17,308][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:04:17,310][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:04:17,310][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:04:17,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:18,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:19,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:19,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:20,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:22,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:23,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:24,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:25,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:25,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:26,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:27,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:28,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:29,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:29,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:30,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:31,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:32,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:33,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:34,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:35,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:35,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:36,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:37,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:38,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:39,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:39,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:40,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:41,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:42,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:43,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:43,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:04:45,454][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:04:46,433][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:04:46,434][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:04:47,751][__main__][INFO] - Iteration 56 took 56s (37.58% Gen, 62.41% Train). Generation: 21s, Training: 35s. Estimated remaining time: 14h 50m 23s. Estimated total time: 15h 44m 39s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 27s, 500 more iterations: 7h 52m 19s. +[2025-08-20 09:04:47,753][__main__][INFO] - Starting iteration 56. +[2025-08-20 09:05:11,130][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:05:11,131][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:05:11,137][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:05:13,561][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:05:13,562][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:05:13,568][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:05:13,570][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:05:13,571][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:05:13,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:14,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:15,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:16,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:17,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:17,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:18,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:19,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:20,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:21,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:21,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:22,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:23,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:24,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:24,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:25,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:26,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:27,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:28,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:28,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:29,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:30,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:31,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:32,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:32,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:33,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:34,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:35,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:36,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:37,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:38,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:39,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:05:40,607][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:05:41,723][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:05:41,725][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:05:43,102][__main__][INFO] - Iteration 57 took 55s (37.85% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 27m 17s. Estimated total time: 15h 22m 29s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 14s. +[2025-08-20 09:05:43,104][__main__][INFO] - Starting iteration 57. +[2025-08-20 09:06:07,652][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:06:07,653][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:06:07,660][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:06:10,109][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:06:10,111][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:06:10,117][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:06:10,119][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:06:10,120][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:06:10,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:11,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:11,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:12,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:13,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:14,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:15,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:15,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:16,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:17,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:18,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:19,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:19,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:20,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:21,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:22,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:23,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:23,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:24,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:25,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:26,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:27,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:28,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:29,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:29,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:30,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:31,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:32,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:33,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:33,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:34,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:35,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:06:37,121][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:06:38,112][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:06:38,113][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:06:39,458][__main__][INFO] - Iteration 58 took 56s (39.20% Gen, 60.80% Train). Generation: 22s, Training: 34s. Estimated remaining time: 14h 43m 5s. Estimated total time: 15h 39m 13s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 55s, 500 more iterations: 7h 49m 36s. +[2025-08-20 09:06:39,460][__main__][INFO] - Starting iteration 58. +[2025-08-20 09:07:04,486][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:07:04,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:07:04,494][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:07:06,964][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:07:06,966][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:07:06,972][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:07:06,974][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:07:06,975][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:07:07,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:08,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:08,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:09,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:10,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:11,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:12,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:12,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:13,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:14,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:15,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:15,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:16,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:17,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:18,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:19,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:19,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:20,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:21,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:22,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:23,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:24,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:25,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:26,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:26,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:27,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:28,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:29,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:29,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:30,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:31,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:32,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:07:33,982][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:07:34,947][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:07:34,948][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:07:36,283][__main__][INFO] - Iteration 59 took 56s (39.70% Gen, 60.29% Train). Generation: 22s, Training: 34s. Estimated remaining time: 14h 49m 58s. Estimated total time: 15h 47m 3s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 42s, 500 more iterations: 7h 53m 31s. +[2025-08-20 09:07:36,849][__main__][INFO] - Starting iteration 59. +[2025-08-20 09:08:00,858][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:00,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:00,865][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:03,328][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:03,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:03,336][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:03,338][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:08:03,338][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:08:03,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:04,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:05,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:06,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:06,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:07,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:08,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:09,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:09,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:10,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:11,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:12,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:13,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:13,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:14,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:15,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:16,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:17,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:17,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:19,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:19,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:20,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:21,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:22,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:23,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:23,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:25,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:25,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:26,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:27,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:28,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:29,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:08:30,755][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:08:31,740][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:08:31,742][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:08:33,080][__main__][INFO] - Iteration 60 took 56s (38.32% Gen, 61.68% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 39m 8s. Estimated total time: 15h 37m 10s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 43s, 500 more iterations: 7h 48m 35s. +[2025-08-20 09:08:33,081][__main__][INFO] - Starting iteration 60. +[2025-08-20 09:08:56,807][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:56,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:56,815][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:59,274][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:59,276][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:59,282][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:08:59,284][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:08:59,285][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:08:59,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:00,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:01,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:01,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:02,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:03,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:04,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:05,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:05,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:06,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:07,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:08,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:09,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:09,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:10,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:11,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:12,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:13,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:13,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:15,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:15,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:16,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:17,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:18,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:19,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:19,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:20,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:21,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:22,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:23,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:23,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:24,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:26,314][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:09:27,322][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:09:27,325][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:09:28,917][__main__][INFO] - Iteration 61 took 55s (38.10% Gen, 61.90% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 31m 37s. Estimated total time: 15h 30m 35s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 3s, 500 more iterations: 7h 45m 17s. +[2025-08-20 09:09:28,919][__main__][INFO] - Starting iteration 61. +[2025-08-20 09:09:52,588][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:09:52,590][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:09:52,596][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:09:55,065][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:09:55,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:09:55,072][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:09:55,074][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:09:55,075][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:09:55,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:56,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:56,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:57,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:58,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:09:59,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:00,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:00,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:01,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:02,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:03,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:04,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:04,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:05,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:06,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:07,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:08,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:08,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:09,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:10,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:11,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:12,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:12,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:13,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:14,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:15,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:16,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:17,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:18,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:18,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:19,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:20,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:22,184][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:10:23,146][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:10:23,147][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:10:24,453][__main__][INFO] - Iteration 62 took 55s (38.20% Gen, 61.79% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 25m 39s. Estimated total time: 15h 25m 33s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 33s, 500 more iterations: 7h 42m 46s. +[2025-08-20 09:10:24,454][__main__][INFO] - Starting iteration 62. +[2025-08-20 09:10:47,650][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:10:47,651][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:10:47,658][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:10:50,136][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:10:50,137][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:10:50,143][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:10:50,146][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:10:50,146][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:10:50,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:51,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:52,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:52,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:53,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:54,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:55,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:55,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:56,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:57,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:58,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:59,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:10:59,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:00,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:01,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:02,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:03,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:03,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:04,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:05,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:06,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:07,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:08,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:09,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:09,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:10,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:11,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:12,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:13,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:13,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:14,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:15,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:17,168][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:11:18,114][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:11:18,115][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:11:19,427][__main__][INFO] - Iteration 63 took 54s (37.68% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 15m 23s. Estimated total time: 15h 16m 12s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 6s. +[2025-08-20 09:11:19,428][__main__][INFO] - Starting iteration 63. +[2025-08-20 09:11:43,549][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:11:43,550][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:11:43,557][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:11:46,024][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:11:46,025][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:11:46,032][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:11:46,034][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:11:46,035][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:11:46,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:47,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:47,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:48,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:49,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:50,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:51,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:51,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:52,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:53,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:54,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:55,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:55,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:56,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:57,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:58,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:59,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:11:59,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:00,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:01,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:02,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:03,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:04,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:04,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:05,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:06,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:07,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:08,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:08,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:09,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:10,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:11,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:12,972][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:12:13,973][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:12:13,975][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:12:15,334][__main__][INFO] - Iteration 64 took 55s (38.74% Gen, 61.26% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 30m 1s. Estimated total time: 15h 31m 45s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 10s, 500 more iterations: 7h 45m 52s. +[2025-08-20 09:12:15,335][__main__][INFO] - Starting iteration 64. +[2025-08-20 09:12:38,599][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:12:38,601][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:12:38,607][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:12:41,053][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:12:41,054][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:12:41,061][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:12:41,063][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:12:41,064][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:12:41,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:42,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:42,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:43,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:44,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:45,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:46,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:46,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:47,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:48,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:49,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:50,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:50,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:51,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:52,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:53,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:54,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:55,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:56,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:56,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:57,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:58,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:12:59,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:00,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:00,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:01,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:02,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:03,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:04,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:04,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:05,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:06,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:08,047][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:13:08,979][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:13:08,980][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:13:10,288][__main__][INFO] - Iteration 65 took 54s (37.90% Gen, 62.09% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 13m 13s. Estimated total time: 15h 15m 52s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 56s. +[2025-08-20 09:13:10,290][__main__][INFO] - Starting iteration 65. +[2025-08-20 09:13:33,805][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:13:33,806][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:13:33,813][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:13:36,269][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:13:36,271][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:13:36,277][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:13:36,279][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:13:36,280][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:13:36,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:37,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:38,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:38,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:39,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:40,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:41,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:42,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:42,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:43,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:44,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:45,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:46,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:46,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:47,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:48,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:49,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:50,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:50,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:51,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:52,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:53,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:54,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:55,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:56,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:56,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:57,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:58,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:13:59,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:00,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:00,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:02,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:04,058][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:14:04,995][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:14:04,997][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:14:06,351][__main__][INFO] - Iteration 66 took 56s (37.58% Gen, 62.42% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 30m 45s. Estimated total time: 15h 34m 20s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 26s, 500 more iterations: 7h 47m 10s. +[2025-08-20 09:14:06,353][__main__][INFO] - Starting iteration 66. +[2025-08-20 09:14:30,010][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:14:30,011][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:14:30,018][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:14:32,496][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:14:32,497][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:14:32,504][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:14:32,506][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:14:32,507][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:14:32,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:33,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:34,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:35,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:35,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:36,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:37,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:38,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:39,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:39,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:40,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:41,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:42,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:43,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:43,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:44,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:45,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:46,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:47,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:47,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:49,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:49,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:50,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:51,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:52,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:53,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:53,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:54,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:55,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:56,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:57,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:57,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:14:59,445][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:15:00,404][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:15:00,406][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:15:01,719][__main__][INFO] - Iteration 67 took 55s (38.33% Gen, 61.67% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 18m 15s. Estimated total time: 15h 22m 45s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 22s. +[2025-08-20 09:15:01,721][__main__][INFO] - Starting iteration 67. +[2025-08-20 09:15:24,939][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:15:24,940][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:15:24,946][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:15:27,412][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:15:27,414][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:15:27,420][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:15:27,422][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:15:27,423][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:15:27,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:28,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:29,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:30,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:30,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:31,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:32,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:33,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:34,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:34,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:35,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:36,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:37,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:38,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:38,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:39,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:40,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:41,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:41,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:42,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:44,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:44,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:45,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:46,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:47,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:48,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:48,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:49,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:50,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:51,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:52,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:52,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:15:54,414][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:15:55,352][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:15:55,353][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:15:56,751][__main__][INFO] - Iteration 68 took 55s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 11m 43s. Estimated total time: 15h 17m 9s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 34s. +[2025-08-20 09:15:56,752][__main__][INFO] - Starting iteration 68. +[2025-08-20 09:16:19,880][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:16:19,914][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:16:19,934][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:16:22,384][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:16:22,385][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:16:22,392][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:16:22,394][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:16:22,395][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:16:22,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:23,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:24,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:25,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:25,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:26,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:27,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:28,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:29,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:29,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:30,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:31,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:32,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:33,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:33,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:34,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:35,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:36,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:36,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:38,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:39,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:39,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:40,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:41,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:42,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:43,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:43,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:44,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:45,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:46,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:47,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:47,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:16:49,459][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:16:50,388][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:16:50,389][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:16:51,653][__main__][INFO] - Iteration 69 took 54s (37.67% Gen, 62.32% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 8m 39s. Estimated total time: 15h 15m 0s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 30s. +[2025-08-20 09:16:51,655][__main__][INFO] - Starting iteration 69. +[2025-08-20 09:17:14,900][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:17:14,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:17:14,908][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:17:17,379][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:17:17,380][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:17:17,387][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:17:17,389][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:17:17,389][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:17:17,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:18,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:19,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:20,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:20,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:21,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:22,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:23,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:24,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:24,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:25,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:26,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:27,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:27,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:28,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:29,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:30,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:31,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:31,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:32,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:33,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:34,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:35,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:35,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:36,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:37,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:38,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:39,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:40,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:41,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:41,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:42,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:17:44,310][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:17:45,333][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:17:45,335][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:17:46,679][__main__][INFO] - Iteration 70 took 55s (37.74% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 9m 47s. Estimated total time: 15h 17m 3s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 31s. +[2025-08-20 09:17:46,680][__main__][INFO] - Starting iteration 70. +[2025-08-20 09:18:09,872][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:18:09,874][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:18:09,880][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:18:12,351][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:18:12,353][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:18:12,359][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:18:12,361][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:18:12,362][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:18:12,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:13,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:14,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:15,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:15,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:16,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:17,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:18,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:19,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:19,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:20,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:21,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:22,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:22,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:23,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:24,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:25,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:26,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:26,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:27,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:29,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:29,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:30,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:31,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:32,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:33,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:33,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:34,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:35,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:36,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:37,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:37,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:18:39,459][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:18:40,413][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:18:40,415][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:18:42,078][__main__][INFO] - Iteration 71 took 55s (37.42% Gen, 62.58% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 15m 5s. Estimated total time: 15h 23m 16s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 38s. +[2025-08-20 09:18:42,079][__main__][INFO] - Starting iteration 71. +[2025-08-20 09:19:05,864][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:19:05,865][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:19:05,871][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:19:08,308][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:19:08,309][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:19:08,316][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:19:08,318][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:19:08,319][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:19:08,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:09,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:10,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:10,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:11,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:12,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:13,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:14,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:14,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:15,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:16,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:17,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:18,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:18,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:19,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:20,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:21,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:22,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:22,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:23,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:24,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:25,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:26,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:27,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:28,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:28,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:29,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:30,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:31,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:32,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:32,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:33,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:19:35,296][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:19:36,262][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:19:36,263][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:19:37,738][__main__][INFO] - Iteration 72 took 55s (38.36% Gen, 61.64% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 18m 31s. Estimated total time: 15h 27m 38s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 45s, 500 more iterations: 7h 43m 49s. +[2025-08-20 09:19:37,740][__main__][INFO] - Starting iteration 72. +[2025-08-20 09:20:00,955][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:00,957][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:00,963][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:03,427][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:03,428][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:03,434][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:03,437][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:20:03,437][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:20:03,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:04,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:05,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:06,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:06,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:07,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:08,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:09,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:10,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:10,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:11,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:12,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:13,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:14,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:14,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:15,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:16,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:17,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:18,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:18,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:19,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:20,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:21,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:21,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:22,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:24,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:24,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:25,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:26,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:27,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:28,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:28,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:30,429][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:20:31,400][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:20:31,401][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:20:32,819][__main__][INFO] - Iteration 73 took 55s (37.65% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 7m 56s. Estimated total time: 15h 17m 58s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 59s. +[2025-08-20 09:20:32,821][__main__][INFO] - Starting iteration 73. +[2025-08-20 09:20:56,013][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:56,015][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:56,021][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:58,468][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:58,469][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:58,476][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:20:58,478][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:20:58,479][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:20:58,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:20:59,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:00,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:01,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:01,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:02,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:03,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:04,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:05,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:05,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:06,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:07,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:08,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:09,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:09,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:10,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:11,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:12,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:13,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:14,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:15,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:15,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:16,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:17,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:18,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:19,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:19,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:20,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:21,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:22,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:23,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:23,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:25,461][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:21:26,385][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:21:26,387][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:21:29,495][__main__][INFO] - Iteration 74 took 56s (36.61% Gen, 63.38% Train). Generation: 20s, Training: 35s. Estimated remaining time: 14h 33m 35s. Estimated total time: 15h 44m 33s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 27s, 500 more iterations: 7h 52m 16s. +[2025-08-20 09:21:29,497][__main__][INFO] - Starting iteration 74. +[2025-08-20 09:21:52,487][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:21:52,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:21:52,495][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:21:54,941][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:21:54,942][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:21:54,949][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:21:54,951][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:21:54,951][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:21:55,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:56,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:56,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:57,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:58,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:21:59,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:00,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:00,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:01,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:02,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:03,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:03,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:04,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:05,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:06,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:07,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:07,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:08,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:09,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:10,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:11,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:11,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:12,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:13,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:14,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:15,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:15,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:16,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:17,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:18,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:19,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:20,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:21,964][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:22:23,005][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:22:23,007][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:22:24,416][__main__][INFO] - Iteration 75 took 54s (37.44% Gen, 62.56% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 3m 25s. Estimated total time: 15h 15m 19s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 39s. +[2025-08-20 09:22:24,419][__main__][INFO] - Starting iteration 75. +[2025-08-20 09:22:47,665][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:22:47,667][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:22:47,673][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:22:50,134][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:22:50,136][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:22:50,142][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:22:50,144][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:22:50,145][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:22:50,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:51,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:52,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:52,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:53,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:54,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:55,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:55,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:56,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:57,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:58,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:59,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:22:59,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:00,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:01,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:02,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:03,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:03,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:04,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:05,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:06,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:07,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:08,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:09,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:09,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:10,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:11,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:12,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:13,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:13,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:14,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:15,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:17,166][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:23:18,148][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:23:18,150][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:23:19,429][__main__][INFO] - Iteration 76 took 55s (37.80% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 4m 1s. Estimated total time: 15h 16m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 24s. +[2025-08-20 09:23:19,430][__main__][INFO] - Starting iteration 76. +[2025-08-20 09:23:45,655][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:23:45,656][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:23:45,663][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:23:48,086][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:23:48,088][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:23:48,094][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:23:48,096][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:23:48,096][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:23:48,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:49,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:49,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:50,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:51,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:52,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:53,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:53,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:54,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:55,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:56,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:57,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:57,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:58,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:23:59,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:00,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:01,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:01,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:02,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:03,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:04,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:05,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:06,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:07,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:07,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:08,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:09,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:10,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:11,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:11,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:12,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:13,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:15,059][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:24:15,990][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:24:15,991][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:24:17,610][__main__][INFO] - Iteration 77 took 58s (40.90% Gen, 59.10% Train). Generation: 23s, Training: 34s. Estimated remaining time: 14h 55m 52s. Estimated total time: 16h 9m 39s. Time estimates for 10 more iterations: 9m 41s, 100 more iterations: 1h 36m 57s, 500 more iterations: 8h 4m 49s. +[2025-08-20 09:24:17,612][__main__][INFO] - Starting iteration 77. +[2025-08-20 09:24:41,071][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:24:41,072][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:24:41,079][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:24:43,538][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:24:43,539][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:24:43,546][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:24:43,548][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:24:43,548][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:24:43,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:44,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:45,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:46,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:47,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:47,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:48,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:49,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:50,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:50,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:51,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:52,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:53,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:54,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:54,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:55,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:56,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:57,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:58,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:58,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:24:59,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:00,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:01,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:02,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:03,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:04,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:04,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:05,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:06,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:07,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:08,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:08,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:10,570][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:25:11,576][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:25:11,579][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:25:12,862][__main__][INFO] - Iteration 78 took 55s (38.05% Gen, 61.95% Train). Generation: 21s, Training: 34s. Estimated remaining time: 14h 6m 7s. Estimated total time: 15h 20m 49s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 4s, 500 more iterations: 7h 40m 24s. +[2025-08-20 09:25:12,863][__main__][INFO] - Starting iteration 78. +[2025-08-20 09:25:35,914][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:25:35,916][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:25:35,922][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:25:38,375][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:25:38,377][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:25:38,383][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:25:38,385][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:25:38,386][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:25:38,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:39,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:40,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:41,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:41,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:42,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:43,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:44,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:45,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:45,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:46,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:47,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:48,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:49,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:49,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:50,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:51,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:52,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:52,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:53,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:54,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:55,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:56,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:57,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:58,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:58,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:25:59,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:00,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:01,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:02,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:02,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:03,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:05,365][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:26:06,304][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:26:06,305][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:26:07,800][__main__][INFO] - Iteration 79 took 54s (37.54% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 0m 0s. Estimated total time: 15h 15m 36s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 48s. +[2025-08-20 09:26:07,802][__main__][INFO] - Starting iteration 79. +[2025-08-20 09:26:30,910][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:26:30,911][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:26:30,918][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:26:33,347][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:26:33,348][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:26:33,355][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:26:33,357][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:26:33,358][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:26:33,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:34,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:35,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:36,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:36,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:37,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:38,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:39,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:40,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:40,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:41,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:42,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:43,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:43,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:44,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:45,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:46,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:47,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:47,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:49,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:50,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:50,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:51,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:52,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:53,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:53,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:54,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:55,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:56,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:57,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:57,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:26:58,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:00,396][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:27:01,374][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:27:01,375][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:27:03,042][__main__][INFO] - Iteration 80 took 55s (37.42% Gen, 62.57% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 4m 6s. Estimated total time: 15h 20m 38s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 3s, 500 more iterations: 7h 40m 19s. +[2025-08-20 09:27:03,044][__main__][INFO] - Starting iteration 80. +[2025-08-20 09:27:26,068][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:27:26,069][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:27:26,076][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:27:28,524][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:27:28,526][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:27:28,532][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:27:28,535][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:27:28,535][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:27:28,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:29,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:30,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:31,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:32,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:32,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:33,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:34,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:35,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:35,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:36,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:37,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:38,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:39,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:39,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:40,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:41,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:42,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:43,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:43,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:44,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:45,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:46,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:47,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:48,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:49,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:49,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:50,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:51,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:52,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:53,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:53,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:27:55,586][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:27:56,508][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:27:56,510][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:27:57,768][__main__][INFO] - Iteration 81 took 54s (37.63% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 54m 37s. Estimated total time: 15h 12m 3s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 12s, 500 more iterations: 7h 36m 1s. +[2025-08-20 09:27:57,769][__main__][INFO] - Starting iteration 81. +[2025-08-20 09:28:21,108][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:28:21,109][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:28:21,115][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:28:23,557][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:28:23,559][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:28:23,565][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:28:23,567][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:28:23,568][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:28:23,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:24,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:25,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:26,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:27,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:27,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:28,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:29,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:30,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:31,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:31,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:32,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:33,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:34,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:34,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:35,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:36,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:37,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:38,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:38,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:39,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:40,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:41,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:42,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:43,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:44,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:44,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:45,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:46,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:47,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:48,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:48,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:28:50,540][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:28:51,481][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:28:51,482][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:28:52,841][__main__][INFO] - Iteration 82 took 55s (37.94% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 59m 29s. Estimated total time: 15h 17m 51s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 55s. +[2025-08-20 09:28:52,843][__main__][INFO] - Starting iteration 82. +[2025-08-20 09:29:15,911][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:29:15,912][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:29:15,918][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:29:18,363][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:29:18,365][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:29:18,371][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:29:18,373][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:29:18,374][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:29:18,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:19,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:20,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:21,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:21,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:22,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:23,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:24,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:25,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:25,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:26,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:27,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:28,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:28,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:29,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:30,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:31,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:32,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:32,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:33,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:34,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:35,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:36,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:37,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:38,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:39,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:39,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:40,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:41,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:42,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:42,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:43,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:29:45,403][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:29:46,337][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:29:46,338][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:29:47,608][__main__][INFO] - Iteration 83 took 54s (37.69% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 53m 28s. Estimated total time: 15h 12m 44s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 16s, 500 more iterations: 7h 36m 22s. +[2025-08-20 09:29:47,611][__main__][INFO] - Starting iteration 83. +[2025-08-20 09:30:11,949][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:30:11,950][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:30:11,956][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:30:14,410][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:30:14,411][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:30:14,418][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:30:14,420][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:30:14,421][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:30:14,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:15,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:16,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:17,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:17,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:18,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:19,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:20,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:21,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:21,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:22,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:23,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:24,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:25,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:25,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:26,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:27,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:28,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:29,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:30,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:31,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:31,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:32,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:33,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:34,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:35,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:35,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:36,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:37,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:38,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:39,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:39,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:30:41,393][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:30:42,431][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:30:42,434][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:30:46,259][__main__][INFO] - Iteration 84 took 58s (37.34% Gen, 62.66% Train). Generation: 21s, Training: 36s. Estimated remaining time: 14h 57m 13s. Estimated total time: 16h 17m 28s. Time estimates for 10 more iterations: 9m 46s, 100 more iterations: 1h 37m 44s, 500 more iterations: 8h 8m 44s. +[2025-08-20 09:30:46,261][__main__][INFO] - Starting iteration 84. +[2025-08-20 09:31:10,846][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:31:10,848][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:31:10,854][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:31:13,290][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:31:13,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:31:13,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:31:13,300][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:31:13,301][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:31:13,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:14,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:15,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:15,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:16,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:17,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:18,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:19,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:19,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:20,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:21,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:22,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:23,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:23,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:24,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:25,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:26,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:27,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:27,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:28,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:29,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:30,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:31,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:32,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:33,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:33,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:34,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:35,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:36,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:37,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:37,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:38,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:31:40,331][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:31:41,258][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:31:41,260][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:31:42,532][__main__][INFO] - Iteration 85 took 56s (39.33% Gen, 60.66% Train). Generation: 22s, Training: 34s. Estimated remaining time: 14h 16m 39s. Estimated total time: 15h 37m 50s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 47s, 500 more iterations: 7h 48m 55s. +[2025-08-20 09:31:42,535][__main__][INFO] - Starting iteration 85. +[2025-08-20 09:32:05,700][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:32:05,701][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:32:05,708][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:32:08,165][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:32:08,166][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:32:08,172][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:32:08,175][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:32:08,175][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:32:08,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:09,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:10,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:10,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:11,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:12,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:13,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:14,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:14,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:15,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:16,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:17,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:18,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:18,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:19,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:20,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:21,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:22,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:22,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:23,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:24,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:25,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:26,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:27,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:28,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:28,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:29,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:30,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:31,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:32,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:32,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:33,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:32:35,256][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:32:36,249][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:32:36,251][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:32:37,552][__main__][INFO] - Iteration 86 took 55s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 54m 50s. Estimated total time: 15h 16m 57s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 28s. +[2025-08-20 09:32:37,554][__main__][INFO] - Starting iteration 86. +[2025-08-20 09:33:00,831][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:00,832][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:00,838][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:03,293][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:03,294][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:03,300][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:03,302][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:33:03,303][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:33:03,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:04,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:05,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:05,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:06,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:07,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:08,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:09,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:09,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:10,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:11,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:12,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:13,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:13,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:14,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:15,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:16,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:17,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:17,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:18,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:19,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:20,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:21,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:22,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:23,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:23,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:24,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:25,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:26,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:27,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:27,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:28,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:30,446][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:33:31,392][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:33:31,393][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:33:32,818][__main__][INFO] - Iteration 87 took 55s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 58m 2s. Estimated total time: 15h 21m 3s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 31s. +[2025-08-20 09:33:32,820][__main__][INFO] - Starting iteration 87. +[2025-08-20 09:33:56,371][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:56,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:56,378][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:58,848][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:58,850][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:58,857][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:33:58,859][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:33:58,860][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:33:59,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:33:59,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:00,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:01,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:02,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:03,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:04,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:04,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:05,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:06,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:07,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:08,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:08,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:09,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:10,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:11,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:12,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:12,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:13,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:14,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:15,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:16,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:17,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:18,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:18,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:19,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:20,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:21,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:22,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:22,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:23,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:24,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:26,061][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:34:27,008][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:34:27,010][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:34:28,252][__main__][INFO] - Iteration 88 took 55s (38.07% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 59m 54s. Estimated total time: 15h 23m 51s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 23s, 500 more iterations: 7h 41m 55s. +[2025-08-20 09:34:28,253][__main__][INFO] - Starting iteration 88. +[2025-08-20 09:34:51,482][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:34:51,484][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:34:51,490][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:34:53,957][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:34:53,958][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:34:53,965][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:34:53,967][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:34:53,968][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:34:54,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:55,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:55,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:56,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:57,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:58,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:59,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:34:59,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:00,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:01,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:02,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:02,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:03,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:04,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:05,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:06,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:06,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:07,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:08,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:09,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:10,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:10,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:12,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:12,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:13,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:14,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:17,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:18,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:18,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:19,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:20,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:21,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:22,912][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:35:23,834][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:35:23,835][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:35:25,369][__main__][INFO] - Iteration 89 took 57s (36.36% Gen, 63.63% Train). Generation: 20s, Training: 36s. Estimated remaining time: 14h 27m 1s. Estimated total time: 15h 51m 55s. Time estimates for 10 more iterations: 9m 31s, 100 more iterations: 1h 35m 11s, 500 more iterations: 7h 55m 57s. +[2025-08-20 09:35:25,371][__main__][INFO] - Starting iteration 89. +[2025-08-20 09:35:48,512][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:35:48,513][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:35:48,520][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:35:50,997][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:35:50,999][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:35:51,005][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:35:51,007][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:35:51,008][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:35:51,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:52,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:52,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:53,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:54,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:55,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:56,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:56,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:57,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:58,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:35:59,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:00,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:00,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:01,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:02,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:03,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:04,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:04,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:05,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:06,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:07,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:08,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:09,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:10,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:10,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:11,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:12,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:13,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:14,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:14,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:15,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:16,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:18,049][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:36:18,968][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:36:18,969][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:36:20,221][__main__][INFO] - Iteration 90 took 54s (37.71% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 48m 20s. Estimated total time: 15h 14m 9s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 4s. +[2025-08-20 09:36:20,222][__main__][INFO] - Starting iteration 90. +[2025-08-20 09:36:43,219][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:36:43,220][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:36:43,226][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:36:45,688][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:36:45,689][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:36:45,696][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:36:45,698][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:36:45,698][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:36:45,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:46,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:47,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:48,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:49,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:49,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:50,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:51,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:52,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:53,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:53,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:54,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:55,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:56,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:57,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:57,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:59,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:36:59,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:00,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:01,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:02,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:03,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:03,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:04,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:05,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:06,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:07,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:07,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:08,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:09,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:10,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:11,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:12,674][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:37:13,641][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:37:13,642][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:37:15,097][__main__][INFO] - Iteration 91 took 54s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 47m 50s. Estimated total time: 15h 14m 34s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 17s. +[2025-08-20 09:37:15,098][__main__][INFO] - Starting iteration 91. +[2025-08-20 09:37:38,768][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:37:38,770][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:37:38,776][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:37:41,222][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:37:41,223][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:37:41,229][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:37:41,232][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:37:41,232][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:37:41,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:42,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:43,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:43,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:44,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:45,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:46,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:47,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:47,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:48,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:49,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:50,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:51,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:51,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:53,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:54,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:55,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:56,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:56,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:57,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:58,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:59,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:37:59,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:01,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:02,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:02,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:03,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:04,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:05,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:06,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:06,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:07,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:09,169][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:38:10,110][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:38:10,112][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:38:11,388][__main__][INFO] - Iteration 92 took 56s (37.72% Gen, 62.28% Train). Generation: 21s, Training: 35s. Estimated remaining time: 14h 10m 28s. Estimated total time: 15h 38m 8s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 48s, 500 more iterations: 7h 49m 4s. +[2025-08-20 09:38:11,389][__main__][INFO] - Starting iteration 92. +[2025-08-20 09:38:34,503][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:38:34,505][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:38:34,511][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:38:36,959][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:38:36,960][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:38:36,966][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:38:36,969][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:38:36,969][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:38:37,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:38,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:38,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:39,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:40,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:41,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:42,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:42,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:43,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:44,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:45,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:45,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:46,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:47,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:48,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:49,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:49,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:50,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:51,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:52,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:53,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:54,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:55,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:55,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:56,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:57,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:58,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:59,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:38:59,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:00,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:01,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:02,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:03,934][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:39:04,882][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:39:04,883][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:39:06,185][__main__][INFO] - Iteration 93 took 54s (37.76% Gen, 62.24% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 44m 40s. Estimated total time: 15h 13m 15s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 19s, 500 more iterations: 7h 36m 37s. +[2025-08-20 09:39:06,187][__main__][INFO] - Starting iteration 93. +[2025-08-20 09:39:29,226][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:39:29,227][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:39:29,233][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:39:31,677][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:39:31,678][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:39:31,685][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:39:31,687][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:39:31,688][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:39:31,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:32,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:33,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:34,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:35,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:35,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:36,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:37,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:38,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:39,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:39,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:40,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:41,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:42,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:43,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:43,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:44,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:45,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:46,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:47,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:47,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:48,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:49,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:50,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:51,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:52,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:53,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:53,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:54,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:55,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:56,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:57,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:39:58,730][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:39:59,654][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:39:59,655][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:40:00,999][__main__][INFO] - Iteration 94 took 54s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 44m 1s. Estimated total time: 15h 13m 31s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 45s. +[2025-08-20 09:40:01,000][__main__][INFO] - Starting iteration 94. +[2025-08-20 09:40:24,120][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:40:24,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:40:24,128][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:40:26,604][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:40:26,605][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:40:26,611][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:40:26,614][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:40:26,614][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:40:26,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:27,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:28,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:29,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:30,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:30,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:31,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:32,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:33,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:34,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:34,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:35,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:36,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:37,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:38,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:38,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:39,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:40,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:41,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:42,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:42,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:43,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:44,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:45,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:45,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:46,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:48,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:48,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:49,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:50,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:51,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:52,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:40:53,597][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:40:54,636][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:40:54,638][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:40:55,920][__main__][INFO] - Iteration 95 took 54s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 44m 54s. Estimated total time: 15h 15m 19s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 39s. +[2025-08-20 09:40:55,922][__main__][INFO] - Starting iteration 95. +[2025-08-20 09:41:18,957][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:41:18,958][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:41:18,964][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:41:21,410][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:41:21,412][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:41:21,418][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:41:21,421][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:41:21,421][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:41:21,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:22,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:23,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:24,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:24,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:25,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:26,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:27,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:28,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:28,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:29,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:30,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:31,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:32,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:32,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:33,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:34,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:35,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:36,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:36,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:37,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:38,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:39,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:40,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:40,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:42,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:42,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:43,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:44,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:45,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:46,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:46,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:41:48,353][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:41:49,296][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:41:49,297][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:41:50,664][__main__][INFO] - Iteration 96 took 54s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 41m 2s. Estimated total time: 15h 12m 21s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 14s, 500 more iterations: 7h 36m 10s. +[2025-08-20 09:41:50,665][__main__][INFO] - Starting iteration 96. +[2025-08-20 09:42:13,736][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:42:13,738][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:42:13,744][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:42:16,203][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:42:16,204][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:42:16,211][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:42:16,213][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:42:16,213][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:42:16,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:17,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:18,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:18,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:19,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:20,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:21,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:22,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:22,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:23,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:24,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:25,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:26,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:26,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:27,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:28,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:29,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:29,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:30,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:31,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:32,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:33,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:34,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:35,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:36,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:36,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:37,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:38,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:39,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:40,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:40,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:41,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:42:43,176][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:42:44,186][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:42:44,189][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:42:45,443][__main__][INFO] - Iteration 97 took 54s (37.65% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 40m 43s. Estimated total time: 15h 12m 57s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 17s, 500 more iterations: 7h 36m 28s. +[2025-08-20 09:42:45,445][__main__][INFO] - Starting iteration 97. +[2025-08-20 09:43:08,995][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:43:08,996][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:43:09,003][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:43:11,467][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:43:11,468][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:43:11,474][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:43:11,477][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:43:11,477][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:43:11,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:12,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:13,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:14,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:14,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:15,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:16,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:17,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:18,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:18,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:19,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:20,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:21,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:22,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:22,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:24,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:24,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:25,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:26,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:27,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:28,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:28,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:29,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:30,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:31,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:32,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:32,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:33,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:34,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:35,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:36,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:36,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:43:38,407][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:43:39,342][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:43:39,343][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:43:41,915][__main__][INFO] - Iteration 98 took 56s (37.36% Gen, 62.64% Train). Generation: 21s, Training: 35s. Estimated remaining time: 14h 7m 58s. Estimated total time: 15h 41m 9s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 6s, 500 more iterations: 7h 50m 34s. +[2025-08-20 09:43:41,917][__main__][INFO] - Starting iteration 98. +[2025-08-20 09:44:05,019][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:44:05,021][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:44:05,027][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:44:07,474][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:44:07,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:44:07,482][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:44:07,484][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:44:07,485][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:44:07,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:08,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:09,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:10,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:10,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:11,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:12,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:13,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:14,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:14,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:15,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:16,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:17,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:18,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:18,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:19,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:20,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:21,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:22,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:22,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:24,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:24,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:25,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:26,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:27,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:28,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:28,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:29,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:30,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:31,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:32,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:32,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:44:34,382][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:44:35,323][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:44:35,324][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:44:36,675][__main__][INFO] - Iteration 99 took 54s (37.74% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 38m 32s. Estimated total time: 15h 12m 38s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 19s. +[2025-08-20 09:44:36,677][__main__][INFO] - Starting iteration 99. +[2025-08-20 09:45:04,028][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:04,030][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:04,036][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:06,513][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:06,515][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:06,521][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:06,524][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:45:06,524][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:45:06,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:07,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:08,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:09,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:09,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:10,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:11,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:12,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:13,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:13,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:14,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:15,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:16,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:17,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:17,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:18,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:19,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:20,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:21,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:21,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:22,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:23,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:24,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:25,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:26,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:27,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:27,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:28,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:29,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:30,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:31,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:31,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:45:33,474][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:45:34,430][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:45:34,431][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:45:35,776][__main__][INFO] - Iteration 100 took 59s (42.10% Gen, 57.90% Train). Generation: 24s, Training: 34s. Estimated remaining time: 14h 49m 53s. Estimated total time: 16h 24m 58s. Time estimates for 10 more iterations: 9m 50s, 100 more iterations: 1h 38m 29s, 500 more iterations: 8h 12m 29s. +[2025-08-20 09:45:35,777][__main__][INFO] - Starting iteration 100. +[2025-08-20 09:45:58,929][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:58,930][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:45:58,936][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:46:01,386][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:46:01,387][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:46:01,393][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:46:01,396][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:46:01,396][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:46:01,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:02,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:03,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:04,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:04,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:05,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:06,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:07,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:08,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:08,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:09,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:10,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:11,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:12,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:12,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:13,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:14,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:15,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:15,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:16,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:17,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:18,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:19,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:20,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:21,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:22,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:22,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:23,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:24,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:25,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:25,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:26,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:46:28,350][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:46:29,302][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:46:29,304][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:46:33,802][__main__][INFO] - Iteration 101 took 58s (35.67% Gen, 58.96% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14h 31m 1s. Estimated total time: 16h 7m 4s. Time estimates for 10 more iterations: 9m 40s, 100 more iterations: 1h 36m 42s, 500 more iterations: 8h 3m 32s. +[2025-08-20 09:46:33,804][__main__][INFO] - Starting iteration 101. +[2025-08-20 09:46:57,387][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:46:59,767][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:46:59,775][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:02,252][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:02,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:02,259][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:02,261][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:47:02,262][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:47:02,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:03,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:04,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:04,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:05,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:06,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:07,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:08,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:08,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:09,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:10,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:11,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:12,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:12,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:13,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:14,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:15,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:16,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:16,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:17,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:18,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:19,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:20,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:21,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:22,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:22,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:23,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:24,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:25,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:26,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:26,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:27,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:29,213][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:47:30,153][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:47:30,155][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:47:31,475][__main__][INFO] - Iteration 102 took 57s (36.60% Gen, 63.39% Train). Generation: 21s, Training: 36s. Estimated remaining time: 14h 24m 9s. Estimated total time: 16h 1m 10s. Time estimates for 10 more iterations: 9m 36s, 100 more iterations: 1h 36m 7s, 500 more iterations: 8h 0m 35s. +[2025-08-20 09:47:31,476][__main__][INFO] - Starting iteration 102. +[2025-08-20 09:47:54,593][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:54,594][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:54,600][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:57,074][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:57,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:57,082][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:47:57,084][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:47:57,085][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:47:57,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:58,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:58,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:47:59,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:00,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:01,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:02,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:02,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:03,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:04,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:05,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:06,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:06,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:07,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:08,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:09,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:10,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:10,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:11,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:12,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:13,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:14,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:14,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:15,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:16,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:17,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:18,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:19,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:20,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:20,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:21,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:22,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:23,988][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:48:28,598][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:48:28,600][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:48:29,956][__main__][INFO] - Iteration 103 took 58s (35.32% Gen, 64.68% Train). Generation: 20s, Training: 37s. Estimated remaining time: 14h 36m 40s. Estimated total time: 16h 14m 39s. Time estimates for 10 more iterations: 9m 44s, 100 more iterations: 1h 37m 27s, 500 more iterations: 8h 7m 19s. +[2025-08-20 09:48:29,957][__main__][INFO] - Starting iteration 103. +[2025-08-20 09:48:53,173][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:48:53,174][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:48:53,181][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:48:55,663][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:48:55,664][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:48:55,671][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:48:55,673][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:48:55,674][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:48:55,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:56,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:57,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:58,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:59,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:48:59,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:00,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:01,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:02,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:03,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:03,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:04,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:05,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:06,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:07,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:07,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:08,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:09,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:10,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:11,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:11,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:12,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:13,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:14,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:15,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:16,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:17,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:17,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:18,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:19,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:20,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:21,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:22,645][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:49:23,552][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:49:23,554][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:49:24,839][__main__][INFO] - Iteration 104 took 54s (37.80% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 35m 47s. Estimated total time: 15h 14m 41s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 20s. +[2025-08-20 09:49:24,841][__main__][INFO] - Starting iteration 104. +[2025-08-20 09:49:48,099][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:49:48,101][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:49:48,108][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:49:50,571][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:49:50,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:49:50,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:49:50,581][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:49:50,581][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:49:50,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:51,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:52,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:53,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:54,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:54,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:55,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:56,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:57,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:58,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:58,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:49:59,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:00,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:01,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:01,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:02,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:03,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:04,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:05,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:05,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:06,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:07,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:08,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:09,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:10,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:11,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:11,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:12,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:13,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:14,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:15,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:15,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:17,495][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:50:18,424][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:50:18,426][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:50:19,702][__main__][INFO] - Iteration 105 took 54s (37.93% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 34m 32s. Estimated total time: 15h 14m 21s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 10s. +[2025-08-20 09:50:19,704][__main__][INFO] - Starting iteration 105. +[2025-08-20 09:50:42,899][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:50:42,900][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:50:42,907][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:50:45,376][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:50:45,377][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:50:45,384][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:50:45,387][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:50:45,387][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:50:45,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:46,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:47,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:48,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:48,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:49,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:50,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:51,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:52,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:52,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:53,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:54,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:55,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:56,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:56,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:57,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:58,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:59,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:50:59,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:00,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:02,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:02,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:03,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:04,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:05,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:05,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:06,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:07,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:08,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:09,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:09,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:10,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:12,310][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:51:13,209][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:51:13,210][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:51:14,523][__main__][INFO] - Iteration 106 took 54s (37.82% Gen, 62.17% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 32m 55s. Estimated total time: 15h 13m 38s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 49s. +[2025-08-20 09:51:14,524][__main__][INFO] - Starting iteration 106. +[2025-08-20 09:51:37,995][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:51:37,996][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:51:38,003][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:51:40,472][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:51:40,474][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:51:40,481][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:51:40,483][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:51:40,483][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:51:40,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:41,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:42,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:43,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:43,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:44,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:45,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:46,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:47,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:47,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:48,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:49,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:50,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:51,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:51,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:52,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:53,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:54,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:55,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:56,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:57,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:57,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:58,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:51:59,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:00,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:01,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:01,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:02,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:03,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:04,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:05,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:05,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:07,452][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:52:08,376][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:52:08,377][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:52:09,732][__main__][INFO] - Iteration 107 took 55s (38.04% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 38m 28s. Estimated total time: 15h 20m 6s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 3s. +[2025-08-20 09:52:09,733][__main__][INFO] - Starting iteration 107. +[2025-08-20 09:52:33,389][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:52:33,390][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:52:33,396][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:52:35,856][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:52:35,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:52:35,864][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:52:35,866][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:52:35,867][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:52:36,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:36,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:37,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:38,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:39,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:40,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:40,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:41,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:42,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:43,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:44,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:44,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:45,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:46,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:47,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:48,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:48,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:49,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:50,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:51,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:52,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:52,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:53,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:54,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:55,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:56,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:57,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:58,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:58,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:52:59,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:00,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:01,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:02,773][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:53:03,688][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:53:03,689][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:53:06,040][__main__][INFO] - Iteration 108 took 56s (37.64% Gen, 62.36% Train). Generation: 21s, Training: 35s. Estimated remaining time: 13h 55m 51s. Estimated total time: 15h 38m 26s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 50s, 500 more iterations: 7h 49m 13s. +[2025-08-20 09:53:06,042][__main__][INFO] - Starting iteration 108. +[2025-08-20 09:53:29,380][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:53:29,381][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:53:29,387][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:53:31,844][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:53:31,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:53:31,852][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:53:31,855][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:53:31,855][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:53:32,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:32,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:33,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:34,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:35,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:36,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:36,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:37,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:38,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:39,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:40,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:40,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:41,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:42,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:43,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:44,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:44,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:45,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:46,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:47,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:48,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:48,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:49,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:50,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:51,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:52,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:53,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:54,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:54,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:55,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:56,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:57,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:53:58,887][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:53:59,828][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:53:59,830][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:54:01,105][__main__][INFO] - Iteration 109 took 55s (37.94% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 34m 12s. Estimated total time: 15h 17m 42s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 51s. +[2025-08-20 09:54:01,106][__main__][INFO] - Starting iteration 109. +[2025-08-20 09:54:24,271][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:54:24,272][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:54:24,279][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:54:26,714][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:54:26,716][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:54:26,722][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:54:26,724][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:54:26,725][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:54:27,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:27,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:28,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:29,403][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:38,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:39,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:40,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:41,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:41,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:42,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:43,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:44,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:45,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:46,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:47,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:48,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:49,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:49,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:50,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:52,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:56,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:56,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:57,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:54:59,363][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:32, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:55:00,262][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:55:00,264][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:55:01,603][__main__][INFO] - Iteration 110 took 1m 0s (34.23% Gen, 65.77% Train). Generation: 20s, Training: 39s. Estimated remaining time: 15h 3m 45s. Estimated total time: 16h 48m 16s. Time estimates for 10 more iterations: 10m 4s, 100 more iterations: 1h 40m 49s, 500 more iterations: 8h 24m 8s. +[2025-08-20 09:55:01,604][__main__][INFO] - Starting iteration 110. +[2025-08-20 09:55:24,853][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:55:24,854][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:55:24,860][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:55:27,321][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:55:27,323][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:55:27,329][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:55:27,332][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:55:27,332][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:55:27,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:28,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:29,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:30,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:30,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:31,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:32,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:33,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:33,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:34,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:35,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:36,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:37,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:37,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:38,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:39,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:40,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:41,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:42,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:43,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:43,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:44,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:45,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:46,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:47,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:47,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:48,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:49,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:50,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:51,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:51,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:52,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:55:54,267][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:55:55,218][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:55:55,220][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:55:56,586][__main__][INFO] - Iteration 111 took 54s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 30m 55s. Estimated total time: 15h 16m 21s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 10s. +[2025-08-20 09:55:56,587][__main__][INFO] - Starting iteration 111. +[2025-08-20 09:56:20,558][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:56:20,559][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:56:20,565][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:56:23,011][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:56:23,013][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:56:23,019][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:56:23,021][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:56:23,022][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:56:23,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:24,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:24,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:25,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:26,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:27,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:28,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:28,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:29,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:30,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:31,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:32,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:32,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:33,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:34,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:35,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:36,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:36,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:37,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:38,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:39,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:40,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:41,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:42,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:42,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:43,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:44,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:45,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:46,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:46,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:47,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:48,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:56:49,983][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:56:50,939][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:56:50,940][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:56:52,227][__main__][INFO] - Iteration 112 took 55s (38.69% Gen, 61.30% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 40m 58s. Estimated total time: 15h 27m 19s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 43s, 500 more iterations: 7h 43m 39s. +[2025-08-20 09:56:52,229][__main__][INFO] - Starting iteration 112. +[2025-08-20 09:57:15,566][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:57:15,567][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:57:15,574][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:57:18,033][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:57:18,035][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:57:18,041][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:57:18,043][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:57:18,044][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:57:18,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:19,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:19,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:20,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:21,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:22,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:23,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:23,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:24,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:25,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:26,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:27,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:27,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:28,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:29,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:30,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:31,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:31,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:32,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:33,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:34,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:35,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:36,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:37,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:37,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:38,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:39,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:40,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:40,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:41,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:42,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:43,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:57:44,960][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:57:45,909][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:57:45,911][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:57:47,785][__main__][INFO] - Iteration 113 took 55s (37.58% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 38m 39s. Estimated total time: 15h 25m 55s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 35s, 500 more iterations: 7h 42m 57s. +[2025-08-20 09:57:47,786][__main__][INFO] - Starting iteration 113. +[2025-08-20 09:58:11,023][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:58:11,024][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:58:11,031][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:58:13,499][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:58:13,500][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:58:13,507][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:58:13,509][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:58:13,510][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:58:13,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:14,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:15,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:16,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:16,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:17,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:18,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:19,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:20,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:20,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:21,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:22,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:23,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:24,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:24,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:25,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:26,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:27,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:28,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:29,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:30,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:31,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:31,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:32,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:33,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:34,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:34,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:35,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:36,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:37,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:38,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:38,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:58:40,550][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:58:41,496][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:58:41,497][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:58:42,784][__main__][INFO] - Iteration 114 took 54s (37.80% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 28m 24s. Estimated total time: 15h 16m 36s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 18s. +[2025-08-20 09:58:42,785][__main__][INFO] - Starting iteration 114. +[2025-08-20 09:59:11,669][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:59:11,671][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:59:11,677][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:59:14,109][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:59:14,111][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:59:14,117][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 09:59:14,119][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 09:59:14,120][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 09:59:14,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:15,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:15,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:16,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:17,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:18,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:19,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:19,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:20,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:21,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:22,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:23,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:23,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:24,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:25,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:26,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:27,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:27,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:29,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:29,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:30,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:31,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:32,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:33,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:33,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:34,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:35,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:36,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:37,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:37,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:38,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:39,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 09:59:40,970][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 09:59:41,938][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 09:59:41,939][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 09:59:43,283][__main__][INFO] - Iteration 115 took 1m 0s (43.70% Gen, 56.30% Train). Generation: 26s, Training: 34s. Estimated remaining time: 14h 59m 5s. Estimated total time: 16h 48m 17s. Time estimates for 10 more iterations: 10m 4s, 100 more iterations: 1h 40m 49s, 500 more iterations: 8h 24m 8s. +[2025-08-20 09:59:43,285][__main__][INFO] - Starting iteration 115. +[2025-08-20 10:00:06,571][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:00:06,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:00:06,578][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:00:09,024][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:00:09,026][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:00:09,032][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:00:09,034][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:00:09,035][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:00:09,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:10,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:10,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:11,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:12,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:13,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:14,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:14,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:15,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:16,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:17,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:18,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:18,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:19,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:20,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:21,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:22,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:22,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:23,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:24,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:25,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:26,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:26,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:27,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:28,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:29,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:29,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:31,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:32,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:32,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:33,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:34,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:00:36,021][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:00:37,025][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:00:37,027][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:00:38,285][__main__][INFO] - Iteration 116 took 54s (37.89% Gen, 62.11% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 26m 32s. Estimated total time: 15h 16m 39s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 19s. +[2025-08-20 10:00:42,985][__main__][INFO] - Starting iteration 116. +[2025-08-20 10:01:06,309][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:01:06,310][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:01:06,316][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:01:08,767][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:01:08,768][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:01:08,774][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:01:08,776][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:01:08,777][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:01:09,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:09,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:10,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:11,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:12,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:13,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:13,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:14,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:15,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:16,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:16,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:17,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:18,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:19,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:20,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:20,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:21,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:22,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:23,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:24,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:24,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:25,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:26,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:27,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:28,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:29,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:30,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:31,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:32,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:32,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:33,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:34,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:01:36,112][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:01:37,081][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:01:37,082][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:01:38,411][__main__][INFO] - Iteration 117 took 55s (37.67% Gen, 62.33% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 32m 27s. Estimated total time: 15h 23m 34s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 21s, 500 more iterations: 7h 41m 47s. +[2025-08-20 10:01:38,413][__main__][INFO] - Starting iteration 117. +[2025-08-20 10:02:01,948][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:02:01,949][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:02:01,955][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:02:04,401][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:02:04,402][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:02:04,409][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:02:04,411][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:02:04,412][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:02:04,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:05,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:06,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:07,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:07,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:08,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:09,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:10,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:11,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:11,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:12,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:13,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:14,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:15,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:15,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:16,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:17,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:18,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:19,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:20,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:21,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:21,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:22,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:23,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:24,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:24,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:25,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:26,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:27,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:28,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:28,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:29,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:02:31,371][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:02:32,357][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:02:32,358][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:02:33,699][__main__][INFO] - Iteration 118 took 55s (38.17% Gen, 61.83% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 29m 23s. Estimated total time: 15h 21m 26s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 8s, 500 more iterations: 7h 40m 43s. +[2025-08-20 10:02:33,700][__main__][INFO] - Starting iteration 118. +[2025-08-20 10:03:00,949][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:03:00,951][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:03:00,957][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:03:03,408][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:03:03,409][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:03:03,416][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:03:03,418][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:03:03,419][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:03:03,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:04,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:05,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:06,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:06,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:07,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:08,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:09,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:10,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:10,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:11,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:12,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:13,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:14,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:14,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:15,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:16,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:17,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:17,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:18,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:20,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:20,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:21,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:22,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:23,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:23,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:24,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:25,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:26,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:27,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:27,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:28,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:03:30,360][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:03:31,354][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:03:31,356][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:03:32,663][__main__][INFO] - Iteration 119 took 58s (42.07% Gen, 57.92% Train). Generation: 24s, Training: 34s. Estimated remaining time: 14h 29m 40s. Estimated total time: 16h 22m 42s. Time estimates for 10 more iterations: 9m 49s, 100 more iterations: 1h 38m 16s, 500 more iterations: 8h 11m 21s. +[2025-08-20 10:03:32,672][__main__][INFO] - Starting iteration 119. +[2025-08-20 10:04:05,261][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:04:05,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:04:05,269][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:04:07,731][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:04:07,733][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:04:07,739][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:04:07,741][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:04:07,742][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:04:08,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:08,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:09,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:10,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:11,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:11,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:12,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:13,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:14,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:15,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:15,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:16,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:17,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:18,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:19,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:19,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:20,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:21,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:22,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:23,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:23,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:24,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:25,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:26,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:27,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:28,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:29,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:29,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:30,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:31,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:32,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:33,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:04:34,661][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:04:35,625][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:04:35,626][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:04:36,967][__main__][INFO] - Iteration 120 took 1m 4s (46.87% Gen, 53.13% Train). Generation: 30s, Training: 34s. Estimated remaining time: 15h 57m 29s. Estimated total time: 17h 51m 35s. Time estimates for 10 more iterations: 10m 42s, 100 more iterations: 1h 47m 9s, 500 more iterations: 8h 55m 47s. +[2025-08-20 10:04:36,969][__main__][INFO] - Starting iteration 120. +[2025-08-20 10:05:00,124][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:05:00,125][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:05:00,131][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:05:02,588][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:05:02,589][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:05:02,596][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:05:02,598][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:05:02,598][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:05:02,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:03,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:04,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:05,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:06,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:06,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:09,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:10,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:11,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:15,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:15,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:16,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:17,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:18,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:19,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:19,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:20,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:21,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:22,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:23,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:23,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:24,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:25,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:26,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:27,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:28,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:29,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:29,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:30,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:31,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:32,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:33,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:05:34,780][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:32, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:05:35,769][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:05:35,770][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:05:37,321][__main__][INFO] - Iteration 121 took 1m 0s (34.30% Gen, 65.69% Train). Generation: 20s, Training: 39s. Estimated remaining time: 14h 50m 45s. Estimated total time: 16h 45m 51s. Time estimates for 10 more iterations: 10m 3s, 100 more iterations: 1h 40m 35s, 500 more iterations: 8h 22m 55s. +[2025-08-20 10:05:37,322][__main__][INFO] - Starting iteration 121. +[2025-08-20 10:06:00,400][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:00,402][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:00,408][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:02,863][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:02,864][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:02,871][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:02,873][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:06:02,874][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:06:03,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:03,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:04,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:05,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:06,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:07,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:07,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:08,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:09,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:10,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:11,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:12,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:12,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:13,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:14,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:15,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:16,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:16,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:18,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:18,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:19,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:20,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:21,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:22,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:22,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:23,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:24,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:25,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:26,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:26,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:27,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:28,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:06:30,027][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:06:31,024][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:06:31,025][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:06:32,641][__main__][INFO] - Iteration 122 took 55s (37.28% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 25m 56s. Estimated total time: 15h 21m 58s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 59s. +[2025-08-20 10:06:32,643][__main__][INFO] - Starting iteration 122. +[2025-08-20 10:06:56,769][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:56,771][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:56,777][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:59,211][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:59,213][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:59,219][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:06:59,221][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:06:59,222][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:06:59,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:00,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:01,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:01,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:02,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:03,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:04,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:05,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:05,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:06,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:07,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:08,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:09,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:09,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:10,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:11,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:12,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:13,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:13,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:14,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:15,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:16,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:17,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:17,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:18,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:19,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:20,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:21,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:22,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:23,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:23,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:24,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:26,225][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:07:27,182][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:07:27,184][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:07:29,127][__main__][INFO] - Iteration 123 took 56s (38.41% Gen, 61.59% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 44m 25s. Estimated total time: 15h 41m 23s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 8s, 500 more iterations: 7h 50m 41s. +[2025-08-20 10:07:29,128][__main__][INFO] - Starting iteration 123. +[2025-08-20 10:07:52,219][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:07:52,220][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:07:52,226][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:07:54,655][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:07:54,656][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:07:54,663][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:07:54,665][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:07:54,666][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:07:54,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:55,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:56,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:57,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:58,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:58,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:07:59,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:00,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:01,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:02,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:02,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:03,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:04,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:05,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:06,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:06,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:07,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:08,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:09,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:10,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:11,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:12,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:12,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:13,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:14,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:15,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:16,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:16,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:17,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:18,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:19,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:20,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:21,748][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:08:22,810][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:08:22,812][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:08:24,138][__main__][INFO] - Iteration 124 took 55s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 18m 55s. Estimated total time: 15h 16m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 24s. +[2025-08-20 10:08:24,139][__main__][INFO] - Starting iteration 124. +[2025-08-20 10:08:47,236][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:08:47,237][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:08:47,244][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:08:49,681][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:08:49,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:08:49,689][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:08:49,690][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:08:49,691][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:08:49,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:50,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:51,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:52,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:53,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:53,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:54,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:55,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:56,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:57,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:57,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:58,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:08:59,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:00,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:01,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:01,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:02,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:03,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:04,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:05,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:05,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:06,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:07,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:08,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:09,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:10,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:11,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:11,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:12,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:13,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:14,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:15,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:16,677][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:09:17,619][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:09:17,621][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:09:18,944][__main__][INFO] - Iteration 125 took 54s (37.71% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 14m 35s. Estimated total time: 15h 13m 23s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 41s. +[2025-08-20 10:09:18,945][__main__][INFO] - Starting iteration 125. +[2025-08-20 10:09:42,463][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:09:42,465][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:09:42,471][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:09:44,919][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:09:44,921][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:09:44,927][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:09:44,929][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:09:44,930][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:09:45,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:46,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:46,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:47,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:48,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:49,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:49,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:50,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:51,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:52,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:53,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:53,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:54,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:55,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:56,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:57,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:57,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:58,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:09:59,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:00,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:01,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:01,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:02,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:03,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:04,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:05,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:06,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:07,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:07,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:08,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:09,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:10,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:11,962][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:10:12,926][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:10:12,928][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:10:14,427][__main__][INFO] - Iteration 126 took 55s (37.98% Gen, 62.02% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 24m 58s. Estimated total time: 15h 24m 41s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 28s, 500 more iterations: 7h 42m 20s. +[2025-08-20 10:10:14,429][__main__][INFO] - Starting iteration 126. +[2025-08-20 10:10:37,605][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:10:37,607][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:10:37,613][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:10:40,090][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:10:40,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:10:40,098][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:10:40,100][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:10:40,100][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:10:40,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:41,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:41,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:42,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:43,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:44,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:45,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:45,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:46,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:47,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:48,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:49,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:49,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:50,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:51,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:52,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:53,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:53,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:54,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:55,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:56,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:57,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:57,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:58,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:10:59,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:00,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:01,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:02,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:03,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:03,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:04,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:05,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:07,128][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:11:08,120][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:11:08,122][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:11:09,483][__main__][INFO] - Iteration 127 took 55s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 16m 55s. Estimated total time: 15h 17m 33s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 46s. +[2025-08-20 10:11:09,484][__main__][INFO] - Starting iteration 127. +[2025-08-20 10:11:32,638][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:11:32,640][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:11:32,646][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:11:35,089][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:11:35,090][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:11:35,096][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:11:35,099][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:11:35,099][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:11:35,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:36,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:36,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:37,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:38,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:39,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:40,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:40,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:41,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:42,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:43,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:44,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:44,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:45,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:46,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:47,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:48,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:48,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:49,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:50,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:51,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:52,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:52,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:53,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:54,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:55,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:56,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:57,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:58,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:58,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:11:59,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:00,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:02,067][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:12:03,029][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:12:03,031][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:12:04,538][__main__][INFO] - Iteration 128 took 55s (37.61% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 16m 0s. Estimated total time: 15h 17m 33s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 46s. +[2025-08-20 10:12:04,540][__main__][INFO] - Starting iteration 128. +[2025-08-20 10:12:28,750][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:12:28,751][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:12:28,758][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:12:31,205][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:12:31,206][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:12:31,212][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:12:31,215][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:12:31,215][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:12:31,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:32,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:33,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:33,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:34,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:35,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:36,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:37,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:37,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:38,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:39,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:40,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:41,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:41,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:42,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:43,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:44,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:45,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:45,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:46,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:47,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:48,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:49,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:49,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:50,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:51,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:52,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:53,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:54,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:55,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:55,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:56,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:12:58,291][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:12:59,267][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:12:59,270][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:13:00,638][__main__][INFO] - Iteration 129 took 56s (38.80% Gen, 61.20% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 32m 28s. Estimated total time: 15h 34m 58s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 29s, 500 more iterations: 7h 47m 29s. +[2025-08-20 10:13:00,640][__main__][INFO] - Starting iteration 129. +[2025-08-20 10:13:24,045][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:13:24,046][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:13:24,053][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:13:26,512][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:13:26,513][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:13:26,519][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:13:26,522][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:13:26,522][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:13:26,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:27,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:28,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:29,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:29,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:30,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:31,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:32,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:33,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:33,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:34,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:35,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:36,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:37,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:37,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:38,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:40,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:40,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:41,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:42,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:43,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:44,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:44,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:45,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:46,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:47,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:47,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:48,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:49,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:50,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:51,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:51,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:13:53,657][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:13:54,603][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:13:54,605][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:13:55,999][__main__][INFO] - Iteration 130 took 55s (37.86% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 19m 13s. Estimated total time: 15h 22m 38s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 19s. +[2025-08-20 10:13:56,000][__main__][INFO] - Starting iteration 130. +[2025-08-20 10:14:19,090][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:14:19,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:14:19,098][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:14:21,532][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:14:21,533][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:14:21,540][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:14:21,542][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:14:21,542][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:14:21,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:22,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:23,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:24,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:25,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:25,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:26,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:27,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:28,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:28,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:29,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:30,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:31,357][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:39,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:40,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:41,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:42,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:42,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:43,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:44,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:45,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:46,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:46,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:14:48,541][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:14:49,523][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:14:49,525][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:14:50,862][__main__][INFO] - Iteration 131 took 54s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 10m 1s. Estimated total time: 15h 14m 21s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 10s. +[2025-08-20 10:14:50,864][__main__][INFO] - Starting iteration 131. +[2025-08-20 10:15:14,212][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:15:14,214][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:15:14,220][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:15:16,664][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:15:16,666][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:15:16,672][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:15:16,674][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:15:16,675][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:15:16,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:17,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:18,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:19,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:20,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:20,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:22,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:23,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:24,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:24,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:25,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:26,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:27,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:28,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:29,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:31,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:31,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:32,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:33,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:34,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:35,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:35,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:36,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:37,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:38,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:39,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:39,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:40,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:41,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:42,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:43,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:43,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:15:45,412][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:15:46,439][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:15:46,442][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:15:47,694][__main__][INFO] - Iteration 132 took 56s (36.79% Gen, 63.20% Train). Generation: 20s, Training: 35s. Estimated remaining time: 13h 41m 52s. Estimated total time: 15h 47m 9s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 42s, 500 more iterations: 7h 53m 34s. +[2025-08-20 10:15:47,696][__main__][INFO] - Starting iteration 132. +[2025-08-20 10:16:10,784][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:16:10,785][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:16:10,791][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:16:13,223][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:16:13,225][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:16:13,232][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:16:13,234][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:16:13,234][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:16:13,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:14,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:15,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:15,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:16,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:17,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:18,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:19,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:19,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:20,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:21,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:22,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:23,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:23,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:24,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:25,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:26,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:27,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:27,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:28,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:29,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:30,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:31,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:32,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:33,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:34,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:35,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:36,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:36,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:37,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:38,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:42,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:16:43,965][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:30, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:16:44,943][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:16:44,944][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:16:46,534][__main__][INFO] - Iteration 133 took 58s (35.10% Gen, 64.90% Train). Generation: 20s, Training: 38s. Estimated remaining time: 14h 14m 22s. Estimated total time: 16h 20m 38s. Time estimates for 10 more iterations: 9m 48s, 100 more iterations: 1h 38m 3s, 500 more iterations: 8h 10m 19s. +[2025-08-20 10:16:46,536][__main__][INFO] - Starting iteration 133. +[2025-08-20 10:17:09,890][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:17:09,891][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:17:09,897][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:17:12,363][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:17:12,364][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:17:12,370][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:17:12,372][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:17:12,373][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:17:12,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:13,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:14,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:15,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:15,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:16,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:17,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:18,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:19,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:19,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:20,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:21,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:22,296][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:30,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:31,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:32,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:33,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:33,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:34,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:35,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:36,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:37,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:37,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:17:39,367][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:17:40,325][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:17:40,327][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:17:41,628][__main__][INFO] - Iteration 134 took 55s (37.91% Gen, 62.08% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 11m 0s. Estimated total time: 15h 18m 11s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 5s. +[2025-08-20 10:17:41,630][__main__][INFO] - Starting iteration 134. +[2025-08-20 10:18:04,661][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:04,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:04,669][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:07,121][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:07,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:07,129][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:07,131][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:18:07,132][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:18:07,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:08,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:09,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:09,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:10,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:11,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:12,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:12,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:13,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:14,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:15,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:16,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:16,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:17,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:18,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:19,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:20,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:20,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:21,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:22,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:23,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:24,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:24,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:26,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:27,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:27,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:28,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:29,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:30,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:31,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:31,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:32,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:18:34,224][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:18:35,196][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:18:35,198][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:18:36,535][__main__][INFO] - Iteration 135 took 54s (37.49% Gen, 62.51% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 6m 56s. Estimated total time: 15h 15m 2s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 31s. +[2025-08-20 10:18:36,537][__main__][INFO] - Starting iteration 135. +[2025-08-20 10:18:59,710][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:59,711][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:18:59,718][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:19:02,169][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:19:02,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:19:02,177][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:19:02,180][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:19:02,180][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:19:02,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:03,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:04,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:04,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:05,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:06,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:07,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:08,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:08,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:09,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:10,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:11,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:12,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:12,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:13,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:14,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:15,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:16,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:16,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:17,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:18,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:19,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:20,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:21,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:21,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:22,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:23,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:24,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:25,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:25,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:26,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:27,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:19:29,167][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:19:30,142][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:19:30,143][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:19:31,608][__main__][INFO] - Iteration 136 took 55s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 8m 50s. Estimated total time: 15h 17m 51s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 55s. +[2025-08-20 10:19:31,610][__main__][INFO] - Starting iteration 136. +[2025-08-20 10:20:00,866][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:00,867][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:00,874][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:03,326][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:03,327][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:03,334][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:03,336][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:20:03,337][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:20:03,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:04,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:05,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:06,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:06,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:07,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:08,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:09,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:09,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:10,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:11,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:12,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:13,160][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:21,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:22,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:23,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:23,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:24,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:25,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:26,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:27,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:27,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:28,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:30,257][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:20:31,229][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:20:31,230][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:20:32,693][__main__][INFO] - Iteration 137 took 1m 1s (43.85% Gen, 56.14% Train). Generation: 26s, Training: 34s. Estimated remaining time: 14h 48m 1s. Estimated total time: 16h 58m 3s. Time estimates for 10 more iterations: 10m 10s, 100 more iterations: 1h 41m 48s, 500 more iterations: 8h 29m 1s. +[2025-08-20 10:20:32,698][__main__][INFO] - Starting iteration 137. +[2025-08-20 10:20:55,819][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:55,821][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:55,827][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:58,286][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:58,288][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:58,294][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:20:58,297][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:20:58,297][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:20:58,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:20:59,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:00,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:00,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:01,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:02,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:03,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:04,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:04,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:05,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:06,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:07,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:08,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:08,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:09,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:10,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:11,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:12,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:12,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:14,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:14,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:15,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:16,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:17,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:18,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:18,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:19,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:20,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:21,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:22,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:22,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:23,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:25,341][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:21:26,328][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:21:26,329][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:21:27,606][__main__][INFO] - Iteration 138 took 54s (37.63% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 4m 7s. Estimated total time: 15h 15m 4s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 32s. +[2025-08-20 10:21:27,608][__main__][INFO] - Starting iteration 138. +[2025-08-20 10:21:51,244][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:21:51,246][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:21:51,252][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:21:53,699][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:21:53,701][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:21:53,707][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:21:53,710][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:21:53,710][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:21:54,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:54,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:55,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:56,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:57,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:57,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:58,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:21:59,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:00,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:01,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:01,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:02,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:03,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:04,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:05,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:05,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:06,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:07,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:08,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:09,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:09,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:10,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:11,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:12,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:13,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:13,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:15,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:15,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:16,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:17,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:18,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:19,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:20,673][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:22:21,653][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:22:21,654][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:22:22,980][__main__][INFO] - Iteration 139 took 55s (38.28% Gen, 61.71% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 10m 59s. Estimated total time: 15h 22m 51s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 17s, 500 more iterations: 7h 41m 25s. +[2025-08-20 10:22:22,982][__main__][INFO] - Starting iteration 139. +[2025-08-20 10:22:46,901][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:22:46,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:22:46,908][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:22:49,351][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:22:49,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:22:49,358][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:22:49,361][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:22:49,361][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:22:49,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:50,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:51,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:22:52,031][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:07,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:08,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:09,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:09,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:10,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:11,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:12,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:13,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:13,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:14,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:16,250][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:23:17,210][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:23:17,211][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:23:18,611][__main__][INFO] - Iteration 140 took 55s (38.60% Gen, 61.39% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 14m 21s. Estimated total time: 15h 27m 9s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 42s, 500 more iterations: 7h 43m 34s. +[2025-08-20 10:23:18,613][__main__][INFO] - Starting iteration 140. +[2025-08-20 10:23:42,523][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:23:42,524][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:23:42,531][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:23:45,037][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:23:45,038][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:23:45,045][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:23:45,047][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:23:45,047][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:23:45,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:46,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:46,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:47,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:48,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:49,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:50,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:50,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:51,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:52,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:53,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:54,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:54,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:55,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:56,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:57,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:58,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:58,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:23:59,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:00,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:01,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:02,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:03,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:04,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:04,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:05,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:06,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:07,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:08,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:08,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:09,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:10,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:12,006][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:24:12,981][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:24:12,983][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:24:14,422][__main__][INFO] - Iteration 141 took 55s (38.37% Gen, 61.63% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 16m 25s. Estimated total time: 15h 30m 9s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 0s, 500 more iterations: 7h 45m 4s. +[2025-08-20 10:24:14,424][__main__][INFO] - Starting iteration 141. +[2025-08-20 10:24:40,131][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:24:40,133][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:24:40,139][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:24:42,598][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:24:42,599][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:24:42,605][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:24:42,608][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:24:42,608][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:24:42,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:43,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:44,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:45,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:46,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:46,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:47,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:48,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:49,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:50,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:50,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:51,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:52,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:53,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:54,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:54,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:55,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:56,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:57,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:58,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:58,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:24:59,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:00,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:01,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:02,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:03,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:03,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:04,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:05,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:06,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:07,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:07,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:09,536][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:25:10,485][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:25:10,487][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:25:11,788][__main__][INFO] - Iteration 142 took 57s (40.55% Gen, 59.45% Train). Generation: 23s, Training: 34s. Estimated remaining time: 13h 41m 23s. Estimated total time: 15h 56m 4s. Time estimates for 10 more iterations: 9m 33s, 100 more iterations: 1h 35m 36s, 500 more iterations: 7h 58m 2s. +[2025-08-20 10:25:11,790][__main__][INFO] - Starting iteration 142. +[2025-08-20 10:25:34,948][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:25:34,949][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:25:34,955][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:25:37,413][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:25:37,414][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:25:37,420][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:25:37,423][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:25:37,423][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:25:37,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:38,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:39,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:40,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:40,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:41,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:42,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:43,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:44,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:44,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:45,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:46,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:47,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:48,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:48,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:49,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:50,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:51,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:52,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:53,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:54,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:54,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:55,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:56,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:57,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:58,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:58,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:25:59,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:00,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:01,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:02,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:02,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:04,353][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:26:05,326][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:26:05,327][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:26:06,660][__main__][INFO] - Iteration 143 took 54s (37.74% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 58m 54s. Estimated total time: 15h 14m 30s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 15s. +[2025-08-20 10:26:06,662][__main__][INFO] - Starting iteration 143. +[2025-08-20 10:26:30,902][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:26:30,903][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:26:30,909][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:26:33,414][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:26:33,415][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:26:33,422][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:26:33,424][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:26:33,424][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:26:33,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:34,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:35,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:36,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:36,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:37,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:38,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:39,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:40,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:40,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:41,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:42,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:43,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:44,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:44,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:45,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:46,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:47,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:48,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:48,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:49,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:50,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:51,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:52,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:53,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:54,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:54,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:55,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:56,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:57,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:58,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:26:58,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:00,419][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:27:01,373][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:27:01,375][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:27:02,625][__main__][INFO] - Iteration 144 took 55s (38.84% Gen, 61.15% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 16m 10s. Estimated total time: 15h 32m 42s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 16s, 500 more iterations: 7h 46m 21s. +[2025-08-20 10:27:02,626][__main__][INFO] - Starting iteration 144. +[2025-08-20 10:27:25,829][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:27:25,831][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:27:25,837][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:27:28,304][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:27:28,305][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:27:28,311][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:27:28,314][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:27:28,315][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:27:28,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:29,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:30,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:31,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:32,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:33,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:33,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:34,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:35,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:36,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:37,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:37,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:38,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:39,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:40,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:41,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:41,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:42,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:43,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:44,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:44,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:45,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:46,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:47,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:48,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:48,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:49,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:50,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:51,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:52,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:53,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:27:57,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:00,495][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:32, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:28:01,635][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:28:01,637][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:28:02,972][__main__][INFO] - Iteration 145 took 1m 0s (34.37% Gen, 65.63% Train). Generation: 20s, Training: 39s. Estimated remaining time: 14h 28m 13s. Estimated total time: 16h 45m 45s. Time estimates for 10 more iterations: 10m 3s, 100 more iterations: 1h 40m 34s, 500 more iterations: 8h 22m 52s. +[2025-08-20 10:28:02,974][__main__][INFO] - Starting iteration 145. +[2025-08-20 10:28:26,102][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:28:26,103][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:28:26,109][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:28:28,560][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:28:28,561][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:28:28,567][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:28:28,570][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:28:28,570][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:28:28,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:29,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:30,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:31,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:32,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:32,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:33,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:34,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:35,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:36,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:36,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:37,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:38,399][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:46,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:47,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:48,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:49,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:49,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:50,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:51,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:52,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:53,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:53,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:28:55,475][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:28:56,443][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:28:56,445][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:28:57,747][__main__][INFO] - Iteration 146 took 54s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 54m 26s. Estimated total time: 15h 12m 52s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 17s, 500 more iterations: 7h 36m 26s. +[2025-08-20 10:28:57,749][__main__][INFO] - Starting iteration 146. +[2025-08-20 10:29:21,400][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:29:21,402][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:29:21,408][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:29:23,876][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:29:23,877][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:29:23,884][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:29:23,886][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:29:23,886][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:29:24,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:24,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:25,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:26,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:27,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:28,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:28,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:29,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:30,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:31,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:32,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:32,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:33,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:34,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:35,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:36,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:36,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:37,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:38,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:39,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:40,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:41,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:42,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:42,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:43,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:44,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:45,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:46,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:46,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:47,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:48,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:49,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:29:50,858][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:29:51,886][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:29:51,888][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:29:53,133][__main__][INFO] - Iteration 147 took 55s (38.23% Gen, 61.77% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 3m 41s. Estimated total time: 15h 23m 4s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 18s, 500 more iterations: 7h 41m 32s. +[2025-08-20 10:29:53,135][__main__][INFO] - Starting iteration 147. +[2025-08-20 10:30:16,421][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:30:16,422][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:30:16,428][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:30:18,907][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:30:18,908][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:30:18,914][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:30:18,917][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:30:18,918][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:30:19,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:20,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:20,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:21,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:22,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:23,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:23,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:24,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:25,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:26,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:27,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:27,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:28,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:29,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:30,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:31,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:31,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:32,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:33,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:34,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:35,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:36,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:37,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:37,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:38,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:39,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:40,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:41,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:41,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:42,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:43,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:44,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:30:45,895][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:30:46,900][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:30:46,902][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:30:48,203][__main__][INFO] - Iteration 148 took 55s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 57m 30s. Estimated total time: 15h 17m 47s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 53s. +[2025-08-20 10:30:48,204][__main__][INFO] - Starting iteration 148. +[2025-08-20 10:31:13,428][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:31:13,429][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:31:13,435][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:31:15,904][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:31:15,905][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:31:15,912][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:31:15,914][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:31:15,915][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:31:16,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:17,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:17,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:19,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:22,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:23,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:23,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:24,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:25,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:26,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:27,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:27,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:28,633][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:37,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:37,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:38,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:39,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:40,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:41,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:41,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:42,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:43,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:44,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:31:45,878][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:29, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:31:46,857][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:31:46,859][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:31:48,140][__main__][INFO] - Iteration 149 took 59s (37.98% Gen, 62.02% Train). Generation: 22s, Training: 37s. Estimated remaining time: 14h 17m 38s. Estimated total time: 16h 38m 55s. Time estimates for 10 more iterations: 9m 59s, 100 more iterations: 1h 39m 53s, 500 more iterations: 8h 19m 27s. +[2025-08-20 10:31:48,141][__main__][INFO] - Starting iteration 149. +[2025-08-20 10:32:11,327][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:32:11,328][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:32:11,334][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:32:13,793][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:32:13,794][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:32:13,801][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:32:13,803][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:32:13,804][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:32:14,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:14,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:15,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:16,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:17,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:18,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:18,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:19,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:20,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:21,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:22,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:22,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:23,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:24,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:25,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:26,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:26,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:27,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:28,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:29,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:30,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:31,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:32,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:32,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:33,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:34,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:35,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:36,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:36,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:37,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:38,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:39,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:32:40,781][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:32:41,742][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:32:41,743][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:32:43,036][__main__][INFO] - Iteration 150 took 54s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 52m 42s. Estimated total time: 15h 14m 54s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 27s. +[2025-08-20 10:32:43,037][__main__][INFO] - Starting iteration 150. +[2025-08-20 10:33:06,190][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:33:06,192][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:33:06,198][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:33:08,629][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:33:08,631][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:33:08,637][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:33:08,640][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:33:08,640][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:33:08,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:09,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:10,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:11,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:12,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:12,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:13,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:14,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:15,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:16,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:16,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:17,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:18,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:19,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:20,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:20,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:21,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:22,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:23,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:24,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:25,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:26,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:26,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:27,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:28,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:29,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:30,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:30,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:31,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:32,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:33,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:34,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:33:35,629][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:33:36,576][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:33:36,577][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:33:41,010][__main__][INFO] - Iteration 151 took 57s (35.75% Gen, 58.94% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13h 43m 2s. Estimated total time: 16h 6m 12s. Time estimates for 10 more iterations: 9m 39s, 100 more iterations: 1h 36m 37s, 500 more iterations: 8h 3m 6s. +[2025-08-20 10:33:41,012][__main__][INFO] - Starting iteration 151. +[2025-08-20 10:34:04,744][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:04,746][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:04,752][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:07,217][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:07,218][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:07,225][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:07,227][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:34:07,227][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:34:07,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:08,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:09,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:09,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:10,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:11,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:12,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:13,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:13,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:14,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:15,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:16,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:17,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:17,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:18,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:19,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:20,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:21,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:22,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:23,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:23,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:24,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:25,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:26,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:27,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:27,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:28,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:29,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:30,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:31,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:31,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:32,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:34:34,195][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:34:35,235][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:34:35,237][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:34:36,593][__main__][INFO] - Iteration 152 took 55s (38.25% Gen, 61.75% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 2m 15s. Estimated total time: 15h 26m 20s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 38s, 500 more iterations: 7h 43m 10s. +[2025-08-20 10:34:36,595][__main__][INFO] - Starting iteration 152. +[2025-08-20 10:34:59,808][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:59,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:34:59,815][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:02,285][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:02,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:02,292][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:02,294][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:35:02,295][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:35:02,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:03,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:04,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:04,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:05,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:06,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:07,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:08,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:08,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:09,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:10,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:11,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:12,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:12,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:13,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:14,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:15,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:16,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:16,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:17,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:18,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:19,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:20,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:21,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:22,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:22,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:23,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:24,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:25,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:26,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:26,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:27,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:29,285][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:35:30,243][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:35:30,244][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:35:31,545][__main__][INFO] - Iteration 153 took 54s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 50m 49s. Estimated total time: 15h 15m 49s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 34s, 500 more iterations: 7h 37m 54s. +[2025-08-20 10:35:31,546][__main__][INFO] - Starting iteration 153. +[2025-08-20 10:35:55,181][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:55,182][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:55,189][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:57,641][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:57,643][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:57,649][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:35:57,651][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:35:57,652][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:35:57,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:58,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:35:59,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:00,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:01,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:01,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:02,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:03,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:04,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:05,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:05,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:06,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:07,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:08,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:09,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:09,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:10,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:11,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:12,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:13,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:14,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:15,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:15,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:16,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:17,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:18,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:19,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:19,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:20,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:21,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:22,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:23,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:24,588][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:36:25,577][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:36:25,579][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:36:26,984][__main__][INFO] - Iteration 154 took 55s (38.21% Gen, 61.79% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 58m 1s. Estimated total time: 15h 23m 57s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 23s, 500 more iterations: 7h 41m 58s. +[2025-08-20 10:36:26,986][__main__][INFO] - Starting iteration 154. +[2025-08-20 10:36:50,386][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:36:50,388][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:36:50,394][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:36:52,867][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:36:52,868][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:36:52,874][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:36:52,877][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:36:52,877][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:36:53,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:53,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:54,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:55,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:56,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:57,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:57,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:58,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:36:59,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:00,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:01,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:01,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:02,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:03,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:04,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:05,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:05,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:06,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:07,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:08,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:09,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:09,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:11,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:11,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:12,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:13,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:14,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:15,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:15,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:16,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:17,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:18,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:19,889][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:37:20,832][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:37:20,834][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:37:25,780][__main__][INFO] - Iteration 155 took 58s (35.58% Gen, 64.42% Train). Generation: 20s, Training: 37s. Estimated remaining time: 13h 52m 59s. Estimated total time: 16h 19m 53s. Time estimates for 10 more iterations: 9m 47s, 100 more iterations: 1h 37m 59s, 500 more iterations: 8h 9m 56s. +[2025-08-20 10:37:25,782][__main__][INFO] - Starting iteration 155. +[2025-08-20 10:37:48,979][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:37:48,981][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:37:48,987][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:37:51,419][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:37:51,420][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:37:51,426][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:37:51,429][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:37:51,429][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:37:51,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:52,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:53,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:54,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:54,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:55,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:56,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:57,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:58,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:58,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:37:59,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:00,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:01,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:02,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:02,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:03,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:04,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:05,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:06,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:06,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:08,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:08,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:09,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:10,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:11,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:12,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:12,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:13,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:14,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:15,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:16,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:16,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:18,450][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:38:19,400][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:38:19,401][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:38:20,955][__main__][INFO] - Iteration 156 took 55s (37.63% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 51m 42s. Estimated total time: 15h 19m 32s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 46s. +[2025-08-20 10:38:20,956][__main__][INFO] - Starting iteration 156. +[2025-08-20 10:38:44,209][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:38:44,210][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:38:44,217][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:38:46,653][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:38:46,654][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:38:46,660][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:38:46,663][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:38:46,663][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:38:46,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:47,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:48,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:49,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:50,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:50,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:51,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:52,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:53,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:54,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:54,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:55,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:56,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:57,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:58,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:58,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:38:59,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:00,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:01,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:02,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:02,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:03,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:04,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:05,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:06,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:07,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:07,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:08,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:09,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:10,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:11,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:11,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:13,533][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:39:14,543][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:39:14,545][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:39:15,994][__main__][INFO] - Iteration 157 took 55s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 48m 32s. Estimated total time: 15h 17m 17s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 38s. +[2025-08-20 10:39:15,996][__main__][INFO] - Starting iteration 157. +[2025-08-20 10:39:39,623][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:39:39,624][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:39:39,631][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:39:42,106][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:39:42,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:39:42,114][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:39:42,116][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:39:42,117][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:39:42,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:43,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:43,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:44,795][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:39:59,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:01,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:01,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:02,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:03,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:04,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:05,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:05,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:06,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:07,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:09,105][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:40:10,091][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:40:10,093][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:40:11,499][__main__][INFO] - Iteration 158 took 55s (38.14% Gen, 61.85% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 55m 22s. Estimated total time: 15h 25m 3s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 30s, 500 more iterations: 7h 42m 31s. +[2025-08-20 10:40:11,501][__main__][INFO] - Starting iteration 158. +[2025-08-20 10:40:36,187][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:40:36,188][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:40:36,194][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:40:38,656][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:40:38,657][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:40:38,663][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:40:38,666][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:40:38,666][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:40:38,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:39,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:40,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:41,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:42,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:42,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:43,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:44,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:45,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:46,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:46,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:47,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:48,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:49,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:50,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:50,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:51,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:52,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:53,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:54,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:54,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:55,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:56,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:57,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:58,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:40:59,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:00,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:00,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:01,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:02,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:03,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:04,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:05,708][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:41:06,663][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:41:06,664][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:41:07,948][__main__][INFO] - Iteration 159 took 56s (39.35% Gen, 60.65% Train). Generation: 22s, Training: 34s. Estimated remaining time: 13h 10m 10s. Estimated total time: 15h 40m 47s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 4s, 500 more iterations: 7h 50m 23s. +[2025-08-20 10:41:07,950][__main__][INFO] - Starting iteration 159. +[2025-08-20 10:41:31,305][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:41:31,306][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:41:31,312][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:41:33,777][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:41:33,779][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:41:33,785][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:41:33,787][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:41:33,788][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:41:34,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:34,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:35,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:36,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:37,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:38,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:38,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:39,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:40,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:41,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:42,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:42,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:43,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:44,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:45,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:45,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:46,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:47,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:48,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:49,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:49,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:51,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:51,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:52,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:53,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:54,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:55,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:55,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:56,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:57,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:58,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:41:59,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:00,724][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:42:01,720][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:42:01,722][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:42:03,093][__main__][INFO] - Iteration 160 took 55s (37.89% Gen, 62.11% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 47m 30s. Estimated total time: 15h 19m 2s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 31s. +[2025-08-20 10:42:03,094][__main__][INFO] - Starting iteration 160. +[2025-08-20 10:42:26,559][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:42:26,560][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:42:26,566][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:42:29,010][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:42:29,011][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:42:29,018][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:42:29,020][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:42:29,020][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:42:29,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:30,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:30,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:31,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:32,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:33,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:34,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:34,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:35,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:36,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:37,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:38,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:38,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:39,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:40,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:41,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:42,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:42,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:43,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:44,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:45,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:46,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:46,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:47,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:48,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:49,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:49,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:50,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:52,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:52,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:53,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:54,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:42:56,004][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:42:56,962][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:42:56,963][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:42:58,323][__main__][INFO] - Iteration 161 took 55s (38.08% Gen, 61.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 48m 0s. Estimated total time: 15h 20m 28s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 14s. +[2025-08-20 10:42:58,325][__main__][INFO] - Starting iteration 161. +[2025-08-20 10:43:21,633][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:43:21,634][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:43:21,641][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:43:24,093][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:43:24,095][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:43:24,101][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:43:24,103][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:43:24,104][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:43:24,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:25,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:25,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:26,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:27,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:28,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:29,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:29,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:30,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:31,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:32,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:33,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:33,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:34,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:35,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:36,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:37,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:37,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:39,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:39,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:40,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:41,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:42,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:43,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:43,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:44,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:45,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:46,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:47,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:47,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:48,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:49,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:43:51,120][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:43:52,083][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:43:52,084][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:43:53,523][__main__][INFO] - Iteration 162 took 55s (37.79% Gen, 62.21% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 46m 35s. Estimated total time: 15h 19m 57s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 59s, 500 more iterations: 7h 39m 58s. +[2025-08-20 10:43:53,524][__main__][INFO] - Starting iteration 162. +[2025-08-20 10:44:17,176][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:44:17,177][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:44:17,183][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:44:19,639][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:44:19,640][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:44:19,647][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:44:19,649][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:44:19,649][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:44:19,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:20,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:21,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:22,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:23,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:23,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:24,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:25,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:26,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:27,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:27,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:28,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:29,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:30,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:31,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:31,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:32,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:33,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:34,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:35,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:36,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:37,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:37,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:38,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:39,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:40,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:40,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:41,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:42,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:43,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:44,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:44,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:44:46,567][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:44:47,558][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:44:47,560][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:44:48,897][__main__][INFO] - Iteration 163 took 55s (38.29% Gen, 61.71% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 48m 34s. Estimated total time: 15h 22m 51s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 17s, 500 more iterations: 7h 41m 25s. +[2025-08-20 10:44:48,898][__main__][INFO] - Starting iteration 163. +[2025-08-20 10:45:12,147][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:45:12,149][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:45:12,155][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:45:14,576][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:45:14,577][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:45:14,583][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:45:14,585][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:45:14,586][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:45:14,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:15,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:16,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:17,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:18,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:18,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:19,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:20,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:21,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:22,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:25,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:26,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:27,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:27,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:28,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:29,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:30,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:31,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:31,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:32,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:33,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:34,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:35,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:35,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:37,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:37,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:38,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:39,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:40,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:41,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:41,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:42,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:45:44,378][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:29, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:45:45,341][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:45:45,342][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:45:46,661][__main__][INFO] - Iteration 164 took 57s (36.02% Gen, 63.98% Train). Generation: 20s, Training: 36s. Estimated remaining time: 13h 27m 27s. Estimated total time: 16h 2m 42s. Time estimates for 10 more iterations: 9m 37s, 100 more iterations: 1h 36m 16s, 500 more iterations: 8h 1m 21s. +[2025-08-20 10:45:46,663][__main__][INFO] - Starting iteration 164. +[2025-08-20 10:46:10,451][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:46:10,452][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:46:10,458][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:46:12,921][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:46:12,922][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:46:12,928][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:46:12,930][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:46:12,931][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:46:13,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:14,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:14,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:15,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:16,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:17,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:17,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:18,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:19,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:20,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:21,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:21,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:22,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:23,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:24,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:25,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:26,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:26,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:29,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:30,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:31,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:32,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:33,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:33,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:34,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:35,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:36,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:37,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:38,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:39,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:39,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:40,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:46:42,374][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:29, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:46:43,342][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:46:43,344][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:46:44,723][__main__][INFO] - Iteration 165 took 58s (36.73% Gen, 63.27% Train). Generation: 21s, Training: 36s. Estimated remaining time: 13h 31m 25s. Estimated total time: 16h 7m 39s. Time estimates for 10 more iterations: 9m 40s, 100 more iterations: 1h 36m 45s, 500 more iterations: 8h 3m 49s. +[2025-08-20 10:46:44,724][__main__][INFO] - Starting iteration 165. +[2025-08-20 10:47:07,994][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:47:07,995][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:47:08,001][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:47:10,460][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:47:10,462][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:47:10,468][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:47:10,470][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:47:10,471][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:47:10,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:11,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:12,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:13,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:13,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:14,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:15,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:16,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:17,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:17,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:18,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:19,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:20,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:21,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:21,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:22,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:23,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:24,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:25,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:25,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:26,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:27,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:28,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:29,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:30,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:31,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:31,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:32,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:33,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:34,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:35,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:35,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:47:37,538][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:47:38,513][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:47:38,514][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:47:40,158][__main__][INFO] - Iteration 166 took 55s (37.55% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 46m 44s. Estimated total time: 15h 23m 53s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 23s, 500 more iterations: 7h 41m 56s. +[2025-08-20 10:47:40,160][__main__][INFO] - Starting iteration 166. +[2025-08-20 10:48:03,363][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:03,365][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:03,371][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:05,821][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:05,822][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:05,828][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:05,830][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:48:05,831][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:48:06,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:06,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:07,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:08,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:09,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:10,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:10,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:11,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:12,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:13,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:14,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:14,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:15,657][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:24,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:24,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:25,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:26,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:27,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:28,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:28,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:29,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:30,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:31,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:48:32,939][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:48:33,929][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:48:33,930][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:48:35,262][__main__][INFO] - Iteration 167 took 55s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 40m 17s. Estimated total time: 15h 18m 21s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 50s, 500 more iterations: 7h 39m 10s. +[2025-08-20 10:48:35,263][__main__][INFO] - Starting iteration 167. +[2025-08-20 10:48:58,745][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:58,746][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:48:58,753][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:01,202][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:01,204][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:01,210][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:01,213][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:49:01,213][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:49:01,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:02,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:03,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:03,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:04,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:05,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:06,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:07,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:07,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:08,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:09,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:10,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:11,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:11,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:12,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:13,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:14,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:15,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:15,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:16,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:17,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:18,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:19,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:20,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:21,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:21,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:22,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:23,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:24,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:25,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:25,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:26,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:28,283][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:49:29,265][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:49:29,267][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:49:30,662][__main__][INFO] - Iteration 168 took 55s (37.99% Gen, 62.01% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 44m 18s. Estimated total time: 15h 23m 17s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 38s. +[2025-08-20 10:49:30,663][__main__][INFO] - Starting iteration 168. +[2025-08-20 10:49:53,870][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:53,872][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:53,878][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:56,328][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:56,330][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:56,336][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:49:56,338][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:49:56,339][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:49:56,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:57,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:58,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:59,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:49:59,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:00,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:01,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:02,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:02,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:03,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:04,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:05,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:06,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:06,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:07,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:08,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:09,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:10,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:10,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:11,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:12,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:13,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:14,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:14,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:15,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:16,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:17,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:18,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:19,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:20,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:20,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:21,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:23,374][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:50:24,381][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:50:24,383][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:50:25,901][__main__][INFO] - Iteration 169 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 40m 42s. Estimated total time: 15h 20m 37s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 3s, 500 more iterations: 7h 40m 18s. +[2025-08-20 10:50:25,903][__main__][INFO] - Starting iteration 169. +[2025-08-20 10:50:49,102][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:50:49,103][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:50:49,110][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:50:51,575][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:50:51,577][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:50:51,583][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:50:51,585][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:50:51,586][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:50:51,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:52,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:53,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:54,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:55,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:55,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:56,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:57,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:58,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:59,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:50:59,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:00,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:01,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:02,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:03,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:03,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:04,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:05,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:06,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:06,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:07,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:08,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:09,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:10,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:11,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:12,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:13,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:13,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:14,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:15,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:16,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:17,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:18,629][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:51:19,577][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:51:19,578][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:51:20,819][__main__][INFO] - Iteration 170 took 54s (37.76% Gen, 62.24% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 34m 26s. Estimated total time: 15h 15m 16s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 38s. +[2025-08-20 10:51:20,821][__main__][INFO] - Starting iteration 170. +[2025-08-20 10:51:43,978][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:51:43,979][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:51:43,986][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:51:46,426][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:51:46,428][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:51:46,434][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:51:46,436][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:51:46,437][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:51:46,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:47,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:48,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:49,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:49,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:50,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:51,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:52,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:53,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:53,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:54,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:55,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:56,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:57,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:57,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:58,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:51:59,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:00,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:01,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:02,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:03,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:03,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:04,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:05,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:06,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:07,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:07,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:08,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:09,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:10,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:11,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:11,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:13,435][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:52:14,389][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:52:14,391][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:52:15,859][__main__][INFO] - Iteration 171 took 55s (37.63% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 35m 32s. Estimated total time: 15h 17m 17s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 38s. +[2025-08-20 10:52:15,860][__main__][INFO] - Starting iteration 171. +[2025-08-20 10:52:39,042][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:52:39,043][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:52:39,049][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:52:41,481][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:52:41,483][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:52:41,489][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:52:41,491][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:52:41,492][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:52:41,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:42,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:43,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:44,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:44,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:45,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:46,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:47,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:48,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:48,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:49,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:50,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:51,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:52,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:52,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:53,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:54,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:55,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:56,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:56,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:57,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:58,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:52:59,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:00,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:01,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:02,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:02,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:03,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:04,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:05,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:06,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:06,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:08,497][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:53:09,487][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:53:09,488][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:53:10,801][__main__][INFO] - Iteration 172 took 54s (37.74% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 33m 0s. Estimated total time: 15h 15m 40s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 34s, 500 more iterations: 7h 37m 50s. +[2025-08-20 10:53:10,803][__main__][INFO] - Starting iteration 172. +[2025-08-20 10:53:34,337][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:53:34,338][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:53:34,345][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:53:36,812][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:53:36,813][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:53:36,820][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:53:36,822][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:53:36,823][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:53:37,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:37,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:38,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:39,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:40,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:41,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:41,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:42,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:43,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:44,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:45,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:45,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:46,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:47,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:48,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:49,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:49,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:50,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:51,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:52,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:53,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:54,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:55,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:55,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:56,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:57,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:58,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:59,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:53:59,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:00,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:01,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:02,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:03,868][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:54:04,853][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:54:04,855][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:54:06,155][__main__][INFO] - Iteration 173 took 55s (38.08% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 38m 56s. Estimated total time: 15h 22m 31s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 15s. +[2025-08-20 10:54:06,156][__main__][INFO] - Starting iteration 173. +[2025-08-20 10:54:29,459][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:54:29,460][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:54:29,466][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:54:31,925][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:54:31,926][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:54:31,933][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:54:31,935][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:54:31,935][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:54:32,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:33,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:33,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:34,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:35,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:36,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:36,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:37,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:38,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:39,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:40,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:40,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:41,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:42,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:43,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:44,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:44,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:45,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:46,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:47,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:48,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:49,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:50,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:50,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:51,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:52,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:53,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:54,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:54,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:55,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:56,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:57,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:54:58,898][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:54:59,849][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:54:59,850][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:55:01,164][__main__][INFO] - Iteration 174 took 55s (37.90% Gen, 62.10% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 32m 17s. Estimated total time: 15h 16m 47s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 23s. +[2025-08-20 10:55:01,165][__main__][INFO] - Starting iteration 174. +[2025-08-20 10:55:24,379][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:55:24,381][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:55:24,387][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:55:26,857][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:55:26,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:55:26,864][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:55:26,867][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:55:26,867][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:55:27,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:27,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:28,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:29,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:30,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:31,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:31,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:32,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:33,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:34,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:35,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:35,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:36,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:37,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:38,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:39,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:39,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:40,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:41,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:42,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:43,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:44,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:45,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:45,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:46,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:47,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:48,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:49,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:49,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:50,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:51,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:52,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:55:53,967][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:55:54,958][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:55:55,474][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:56:03,791][__main__][INFO] - Iteration 175 took 1m 2s (33.13% Gen, 66.87% Train). Generation: 20s, Training: 41s. Estimated remaining time: 14h 38m 12s. Estimated total time: 17h 23m 45s. Time estimates for 10 more iterations: 10m 26s, 100 more iterations: 1h 44m 22s, 500 more iterations: 8h 41m 52s. +[2025-08-20 10:56:03,793][__main__][INFO] - Starting iteration 175. +[2025-08-20 10:56:26,869][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:56:26,870][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:56:26,877][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:56:29,351][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:56:29,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:56:29,359][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:56:29,361][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:56:29,362][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:56:29,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:30,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:31,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:32,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:32,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:33,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:34,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:35,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:36,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:36,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:37,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:38,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:39,179][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:47,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:48,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:49,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:50,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:50,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:51,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:52,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:53,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:54,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:54,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:56:56,417][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:56:57,381][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:56:57,382][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:56:58,668][__main__][INFO] - Iteration 176 took 54s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 28m 7s. Estimated total time: 15h 14m 35s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 17s. +[2025-08-20 10:56:58,670][__main__][INFO] - Starting iteration 176. +[2025-08-20 10:57:21,808][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:57:21,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:57:21,815][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:57:24,275][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:57:24,276][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:57:24,282][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:57:24,285][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:57:24,285][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:57:24,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:25,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:26,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:26,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:27,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:28,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:29,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:30,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:30,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:31,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:32,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:33,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:34,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:34,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:35,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:36,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:37,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:38,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:38,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:39,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:40,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:41,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:42,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:42,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:44,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:44,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:45,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:46,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:47,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:48,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:48,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:49,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:57:51,313][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:57:52,320][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:57:52,322][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:57:53,638][__main__][INFO] - Iteration 177 took 54s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 28m 45s. Estimated total time: 15h 16m 8s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 4s. +[2025-08-20 10:57:53,640][__main__][INFO] - Starting iteration 177. +[2025-08-20 10:58:16,751][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:58:16,752][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:58:16,758][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:58:19,232][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:58:19,233][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:58:19,240][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:58:19,242][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:58:19,242][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:58:19,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:20,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:21,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:21,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:22,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:23,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:24,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:25,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:25,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:26,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:27,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:28,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:29,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:29,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:30,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:31,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:32,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:33,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:34,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:35,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:35,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:36,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:37,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:38,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:39,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:39,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:40,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:41,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:42,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:43,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:43,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:44,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:58:46,249][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:58:47,234][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:58:47,235][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:58:48,575][__main__][INFO] - Iteration 178 took 54s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 27m 17s. Estimated total time: 15h 15m 35s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 47s. +[2025-08-20 10:58:48,577][__main__][INFO] - Starting iteration 178. +[2025-08-20 10:59:12,003][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:59:12,005][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:59:12,011][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:59:14,462][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:59:14,463][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:59:14,470][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 10:59:14,472][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 10:59:14,472][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 10:59:14,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:15,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:16,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:17,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:17,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:18,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:19,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:20,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:21,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:21,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:22,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:23,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:24,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:25,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:25,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:26,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:27,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:28,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:29,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:29,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:30,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:31,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:32,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:33,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:33,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:34,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:35,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:36,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:37,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:38,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:39,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:39,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 10:59:41,537][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 10:59:42,539][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 10:59:42,542][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 10:59:44,391][__main__][INFO] - Iteration 179 took 55s (37.57% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 41m 0s. Estimated total time: 15h 30m 13s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 1s, 500 more iterations: 7h 45m 6s. +[2025-08-20 10:59:44,392][__main__][INFO] - Starting iteration 179. +[2025-08-20 11:00:07,524][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:00:07,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:00:07,531][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:00:09,988][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:00:09,989][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:00:09,995][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:00:09,997][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:00:09,998][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:00:10,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:11,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:11,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:12,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:13,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:14,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:15,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:15,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:16,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:17,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:18,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:19,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:19,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:20,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:21,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:22,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:23,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:23,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:24,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:25,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:26,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:27,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:28,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:29,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:29,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:30,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:31,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:32,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:32,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:33,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:34,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:35,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:00:36,977][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:00:37,976][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:00:37,978][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:00:39,286][__main__][INFO] - Iteration 180 took 54s (37.68% Gen, 62.32% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 24m 44s. Estimated total time: 15h 14m 52s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 26s. +[2025-08-20 11:00:39,287][__main__][INFO] - Starting iteration 180. +[2025-08-20 11:01:02,424][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:02,425][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:02,431][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:04,903][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:04,904][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:04,910][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:04,913][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:01:04,913][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:01:05,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:06,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:06,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:07,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:08,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:09,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:09,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:10,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:11,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:12,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:13,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:13,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:14,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:15,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:16,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:17,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:17,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:18,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:19,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:20,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:21,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:21,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:22,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:23,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:24,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:25,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:26,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:27,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:27,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:28,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:29,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:30,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:01:31,968][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:01:32,936][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:01:32,937][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:01:34,251][__main__][INFO] - Iteration 181 took 54s (37.63% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 24m 59s. Estimated total time: 15h 16m 2s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 1s. +[2025-08-20 11:01:34,252][__main__][INFO] - Starting iteration 181. +[2025-08-20 11:01:57,474][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:57,475][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:57,481][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:59,960][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:59,961][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:59,967][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:01:59,970][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:01:59,970][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:02:00,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:01,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:01,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:02,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:03,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:04,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:05,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:05,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:06,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:07,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:08,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:08,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:09,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:10,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:11,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:12,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:12,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:13,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:15,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:15,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:16,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:17,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:18,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:19,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:19,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:20,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:21,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:22,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:23,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:23,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:24,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:28,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:02:30,322][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:30, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:02:31,248][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:02:31,249][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:02:36,217][__main__][INFO] - Iteration 182 took 1m 1s (33.51% Gen, 66.49% Train). Generation: 20s, Training: 41s. Estimated remaining time: 14h 20m 38s. Estimated total time: 17h 12m 44s. Time estimates for 10 more iterations: 10m 19s, 100 more iterations: 1h 43m 16s, 500 more iterations: 8h 36m 22s. +[2025-08-20 11:02:36,218][__main__][INFO] - Starting iteration 182. +[2025-08-20 11:02:59,254][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:02:59,255][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:02:59,261][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:01,695][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:01,696][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:01,702][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:01,705][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:03:01,705][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:03:02,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:02,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:03,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:04,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:05,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:05,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:06,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:07,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:08,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:09,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:09,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:10,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:11,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:12,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:13,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:13,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:14,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:15,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:16,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:17,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:18,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:19,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:19,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:20,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:21,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:22,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:23,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:23,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:24,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:25,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:26,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:27,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:28,677][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:03:29,665][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:03:29,667][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:03:30,986][__main__][INFO] - Iteration 183 took 54s (37.60% Gen, 62.40% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 19m 47s. Estimated total time: 15h 12m 47s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 16s, 500 more iterations: 7h 36m 23s. +[2025-08-20 11:03:30,988][__main__][INFO] - Starting iteration 183. +[2025-08-20 11:03:54,408][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:54,409][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:54,415][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:56,860][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:56,861][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:56,867][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:03:56,869][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:03:56,870][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:03:57,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:57,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:58,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:03:59,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:00,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:01,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:01,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:02,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:03,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:04,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:05,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:05,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:06,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:07,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:08,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:09,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:09,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:10,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:11,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:12,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:13,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:14,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:15,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:15,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:16,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:17,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:18,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:19,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:19,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:20,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:21,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:22,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:23,944][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:04:24,892][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:04:24,893][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:04:27,167][__main__][INFO] - Iteration 184 took 56s (37.34% Gen, 62.66% Train). Generation: 20s, Training: 35s. Estimated remaining time: 12h 42m 22s. Estimated total time: 15h 36m 18s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 37s, 500 more iterations: 7h 48m 9s. +[2025-08-20 11:04:27,168][__main__][INFO] - Starting iteration 184. +[2025-08-20 11:04:50,372][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:04:50,373][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:04:50,380][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:04:52,858][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:04:52,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:04:52,866][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:04:52,868][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:04:52,868][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:04:53,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:53,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:54,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:55,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:56,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:57,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:57,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:58,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:04:59,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:00,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:01,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:01,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:02,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:03,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:04,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:05,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:05,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:06,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:07,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:08,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:09,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:10,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:11,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:12,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:12,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:13,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:14,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:15,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:16,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:16,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:17,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:18,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:20,020][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:05:21,054][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:05:21,056][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:05:30,746][__main__][INFO] - Iteration 185 took 1m 3s (32.60% Gen, 67.40% Train). Generation: 20s, Training: 42s. Estimated remaining time: 14h 44m 37s. Estimated total time: 17h 39m 37s. Time estimates for 10 more iterations: 10m 35s, 100 more iterations: 1h 45m 57s, 500 more iterations: 8h 49m 48s. +[2025-08-20 11:05:30,748][__main__][INFO] - Starting iteration 185. +[2025-08-20 11:05:54,013][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:05:54,014][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:05:54,020][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:05:56,489][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:05:56,491][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:05:56,497][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:05:56,500][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:05:56,500][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:05:56,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:57,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:58,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:59,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:05:59,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:00,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:01,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:02,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:03,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:03,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:04,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:05,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:06,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:07,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:07,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:08,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:09,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:10,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:11,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:11,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:12,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:13,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:14,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:15,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:15,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:16,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:17,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:18,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:19,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:20,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:21,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:21,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:23,427][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:06:24,401][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:06:24,402][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:06:25,740][__main__][INFO] - Iteration 186 took 54s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 20m 37s. Estimated total time: 15h 16m 31s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 15s. +[2025-08-20 11:06:25,742][__main__][INFO] - Starting iteration 186. +[2025-08-20 11:06:48,944][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:06:48,946][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:06:48,952][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:06:51,421][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:06:51,423][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:06:51,430][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:06:51,432][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:06:51,432][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:06:51,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:52,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:53,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:54,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:54,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:55,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:56,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:57,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:58,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:58,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:06:59,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:00,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:01,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:02,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:02,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:03,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:04,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:05,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:06,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:07,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:08,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:08,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:09,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:10,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:11,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:12,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:12,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:13,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:14,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:15,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:16,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:16,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:18,397][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:07:19,333][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:07:19,335][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:07:21,513][__main__][INFO] - Iteration 187 took 55s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 35s. Estimated remaining time: 12h 32m 40s. Estimated total time: 15h 29m 31s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 57s, 500 more iterations: 7h 44m 45s. +[2025-08-20 11:07:21,515][__main__][INFO] - Starting iteration 187. +[2025-08-20 11:07:44,737][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:07:44,739][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:07:44,746][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:07:47,178][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:07:47,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:07:47,186][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:07:47,189][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:07:47,189][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:07:47,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:48,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:49,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:49,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:50,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:51,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:52,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:53,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:53,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:54,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:55,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:56,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:57,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:57,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:58,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:07:59,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:00,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:01,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:01,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:03,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:03,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:04,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:05,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:06,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:07,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:07,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:08,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:09,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:10,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:11,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:11,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:12,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:14,302][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:08:15,251][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:08:15,253][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:08:16,569][__main__][INFO] - Iteration 188 took 55s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 19m 48s. Estimated total time: 15h 17m 33s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 46s. +[2025-08-20 11:08:16,571][__main__][INFO] - Starting iteration 188. +[2025-08-20 11:08:40,610][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:08:40,611][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:08:40,617][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:08:43,085][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:08:43,086][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:08:43,092][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:08:43,094][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:08:43,095][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:08:43,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:44,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:44,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:45,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:46,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:47,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:48,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:48,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:49,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:50,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:51,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:52,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:52,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:54,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:55,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:56,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:56,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:57,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:58,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:08:59,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:00,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:01,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:02,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:02,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:03,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:04,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:05,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:06,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:06,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:07,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:08,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:09,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:10,742][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:09:11,684][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:09:11,687][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:09:13,006][__main__][INFO] - Iteration 189 took 56s (38.22% Gen, 61.78% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 41m 53s. Estimated total time: 15h 40m 35s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 3s, 500 more iterations: 7h 50m 17s. +[2025-08-20 11:09:13,008][__main__][INFO] - Starting iteration 189. +[2025-08-20 11:09:36,484][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:09:36,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:09:36,492][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:09:38,953][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:09:38,955][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:09:38,961][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:09:38,964][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:09:38,964][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:09:39,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:40,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:40,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:41,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:42,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:43,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:44,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:44,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:45,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:46,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:47,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:48,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:48,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:49,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:50,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:51,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:51,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:52,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:53,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:54,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:55,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:55,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:56,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:58,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:58,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:09:59,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:00,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:01,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:02,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:02,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:03,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:04,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:06,012][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:10:06,948][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:10:06,949][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:10:08,500][__main__][INFO] - Iteration 190 took 55s (37.88% Gen, 62.11% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 25m 13s. Estimated total time: 15h 24m 51s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 29s, 500 more iterations: 7h 42m 25s. +[2025-08-20 11:10:08,501][__main__][INFO] - Starting iteration 190. +[2025-08-20 11:10:31,802][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:10:31,804][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:10:31,810][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:10:34,295][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:10:34,297][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:10:34,303][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:10:34,305][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:10:34,306][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:10:34,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:35,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:36,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:36,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:37,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:38,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:39,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:40,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:40,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:41,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:42,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:43,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:44,146][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:52,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:53,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:54,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:54,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:55,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:56,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:57,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:58,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:58,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:10:59,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:01,304][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:11:02,298][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:11:02,299][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:11:03,651][__main__][INFO] - Iteration 191 took 55s (37.79% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 18m 36s. Estimated total time: 15h 19m 9s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 34s. +[2025-08-20 11:11:03,652][__main__][INFO] - Starting iteration 191. +[2025-08-20 11:11:26,855][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:11:26,856][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:11:26,862][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:11:29,309][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:11:29,310][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:11:29,317][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:11:29,319][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:11:29,320][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:11:29,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:30,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:31,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:32,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:32,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:33,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:34,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:35,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:35,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:36,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:37,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:38,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:39,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:39,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:40,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:41,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:42,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:43,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:43,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:44,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:45,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:46,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:47,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:48,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:49,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:50,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:50,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:51,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:52,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:53,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:53,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:54,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:11:56,320][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:11:57,245][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:11:57,247][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:11:59,428][__main__][INFO] - Iteration 192 took 55s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 35s. Estimated remaining time: 12h 28m 6s. Estimated total time: 15h 29m 35s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 57s, 500 more iterations: 7h 44m 47s. +[2025-08-20 11:11:59,429][__main__][INFO] - Starting iteration 192. +[2025-08-20 11:12:22,808][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:12:22,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:12:22,815][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:12:25,311][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:12:25,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:12:25,319][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:12:25,322][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:12:25,322][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:12:25,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:26,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:27,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:28,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:28,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:29,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:30,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:31,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:31,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:32,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:33,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:34,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:35,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:35,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:36,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:37,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:38,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:39,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:39,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:40,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:42,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:42,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:43,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:44,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:45,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:46,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:46,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:47,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:48,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:49,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:49,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:50,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:12:52,316][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:12:53,649][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:12:54,580][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:12:55,924][__main__][INFO] - Iteration 193 took 56s (36.94% Gen, 63.06% Train). Generation: 20s, Training: 35s. Estimated remaining time: 12h 39m 8s. Estimated total time: 15h 41m 33s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 9s, 500 more iterations: 7h 50m 46s. +[2025-08-20 11:12:57,102][__main__][INFO] - Starting iteration 193. +[2025-08-20 11:13:20,666][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:13:20,667][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:13:20,674][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:13:23,144][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:13:23,146][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:13:23,152][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:13:23,155][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:13:23,155][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:13:23,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:24,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:25,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:25,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:26,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:27,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:28,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:29,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:29,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:30,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:31,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:32,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:32,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:33,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:34,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:35,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:36,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:36,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:37,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:38,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:39,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:40,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:40,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:42,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:42,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:43,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:44,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:45,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:46,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:46,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:47,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:48,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:13:50,138][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:13:51,073][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:13:51,075][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:13:52,478][__main__][INFO] - Iteration 194 took 55s (38.10% Gen, 61.90% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 19m 32s. Estimated total time: 15h 22m 53s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 17s, 500 more iterations: 7h 41m 26s. +[2025-08-20 11:13:52,479][__main__][INFO] - Starting iteration 194. +[2025-08-20 11:14:16,181][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:14:16,182][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:14:16,189][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:14:18,649][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:14:18,650][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:14:18,657][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:14:18,659][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:14:18,660][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:14:18,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:19,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:20,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:21,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:22,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:22,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:23,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:24,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:25,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:26,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:26,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:27,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:28,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:29,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:30,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:30,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:31,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:32,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:33,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:34,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:34,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:35,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:36,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:37,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:38,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:39,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:40,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:40,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:41,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:42,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:43,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:44,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:14:45,561][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:14:46,490][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:14:46,491][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:14:47,850][__main__][INFO] - Iteration 195 took 55s (38.38% Gen, 61.62% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 18m 33s. Estimated total time: 15h 22m 49s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 24s. +[2025-08-20 11:14:47,851][__main__][INFO] - Starting iteration 195. +[2025-08-20 11:15:13,508][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:15:15,051][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:15:15,059][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:15:17,530][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:15:17,531][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:15:17,538][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:15:17,540][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:15:17,540][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:15:17,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:18,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:19,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:25,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:26,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:26,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:27,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:28,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:29,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:30,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:30,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:31,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:32,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:33,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:34,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:35,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:37,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:38,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:39,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:40,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:41,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:42,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:42,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:43,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:44,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:45,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:46,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:47,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:48,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:49,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:49,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:50,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:15:52,205][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:34, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:15:53,155][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:15:53,156][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:15:54,634][__main__][INFO] - Iteration 196 took 1m 6s (34.71% Gen, 65.28% Train). Generation: 23s, Training: 43s. Estimated remaining time: 15h 27m 39s. Estimated total time: 18h 33m 2s. Time estimates for 10 more iterations: 11m 7s, 100 more iterations: 1h 51m 18s, 500 more iterations: 9h 16m 31s. +[2025-08-20 11:15:54,636][__main__][INFO] - Starting iteration 196. +[2025-08-20 11:16:18,359][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:16:18,361][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:16:18,367][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:16:20,827][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:16:20,829][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:16:20,836][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:16:20,838][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:16:20,838][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:16:21,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:21,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:22,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:23,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:24,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:25,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:25,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:26,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:27,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:28,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:29,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:29,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:30,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:31,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:32,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:33,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:33,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:34,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:35,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:36,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:37,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:38,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:39,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:39,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:40,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:41,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:42,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:43,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:43,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:44,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:45,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:46,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:16:47,813][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:16:48,751][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:16:48,753][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:16:54,635][__main__][INFO] - Iteration 197 took 59s (35.44% Gen, 64.56% Train). Generation: 21s, Training: 38s. Estimated remaining time: 13h 33m 35s. Estimated total time: 16h 39m 59s. Time estimates for 10 more iterations: 9m 59s, 100 more iterations: 1h 39m 59s, 500 more iterations: 8h 19m 59s. +[2025-08-20 11:16:54,637][__main__][INFO] - Starting iteration 197. +[2025-08-20 11:17:18,099][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:17:18,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:17:18,106][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:17:20,551][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:17:20,552][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:17:20,559][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:17:20,561][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:17:20,562][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:17:20,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:21,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:22,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:23,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:24,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:24,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:25,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:26,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:27,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:27,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:28,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:29,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:30,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:31,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:31,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:32,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:33,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:34,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:35,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:35,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:36,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:37,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:38,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:39,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:40,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:41,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:41,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:42,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:43,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:44,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:45,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:45,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:17:47,428][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:17:48,387][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:17:48,389][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:17:49,759][__main__][INFO] - Iteration 198 took 55s (38.13% Gen, 61.86% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 11m 23s. Estimated total time: 15h 18m 42s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 21s. +[2025-08-20 11:17:49,761][__main__][INFO] - Starting iteration 198. +[2025-08-20 11:18:13,437][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:18:13,438][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:18:13,445][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:18:15,906][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:18:15,907][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:18:15,914][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:18:15,916][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:18:15,916][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:18:16,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:17,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:17,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:18,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:19,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:20,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:20,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:21,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:22,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:23,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:24,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:24,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:25,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:26,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:27,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:28,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:28,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:29,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:30,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:31,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:32,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:33,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:34,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:34,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:35,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:36,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:37,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:38,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:38,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:39,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:40,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:41,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:18:42,901][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:18:43,849][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:18:43,850][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:18:45,488][__main__][INFO] - Iteration 199 took 55s (38.08% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 20m 31s. Estimated total time: 15h 28m 46s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 52s, 500 more iterations: 7h 44m 23s. +[2025-08-20 11:18:45,489][__main__][INFO] - Starting iteration 199. +[2025-08-20 11:19:08,850][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:19:08,851][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:19:08,857][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:19:11,312][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:19:11,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:19:11,319][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:19:11,321][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:19:11,322][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:19:11,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:12,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:13,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:13,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:14,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:15,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:16,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:17,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:17,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:18,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:19,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:20,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:21,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:21,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:22,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:23,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:24,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:25,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:25,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:26,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:27,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:28,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:29,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:30,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:31,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:31,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:32,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:33,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:34,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:35,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:35,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:36,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:19:38,265][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:19:39,237][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:19:39,239][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:19:40,726][__main__][INFO] - Iteration 200 took 55s (37.86% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 11m 27s. Estimated total time: 15h 20m 36s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 3s, 500 more iterations: 7h 40m 18s. +[2025-08-20 11:19:40,728][__main__][INFO] - Starting iteration 200. +[2025-08-20 11:20:04,196][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:20:04,197][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:20:04,203][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:20:06,654][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:20:06,655][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:20:06,661][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:20:06,663][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:20:06,664][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:20:06,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:07,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:08,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:09,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:10,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:10,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:11,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:12,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:13,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:14,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:14,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:15,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:16,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:17,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:18,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:18,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:19,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:20,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:21,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:22,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:22,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:23,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:24,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:25,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:26,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:27,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:28,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:28,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:29,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:30,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:31,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:32,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:20:33,582][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:20:34,552][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:20:34,554][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:20:39,199][__main__][INFO] - Iteration 201 took 58s (35.94% Gen, 58.98% Train). Generation: 21s, Training: 34s. Estimated remaining time: 13h 4m 22s. Estimated total time: 16h 14m 30s. Time estimates for 10 more iterations: 9m 44s, 100 more iterations: 1h 37m 27s, 500 more iterations: 8h 7m 15s. +[2025-08-20 11:20:39,200][__main__][INFO] - Starting iteration 201. +[2025-08-20 11:21:02,507][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:21:02,508][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:21:02,515][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:21:04,993][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:21:04,994][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:21:05,001][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:21:05,003][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:21:05,004][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:21:05,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:06,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:06,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:07,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:08,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:09,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:10,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:10,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:11,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:12,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:13,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:14,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:14,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:15,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:16,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:17,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:18,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:18,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:20,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:20,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:25,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:25,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:26,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:27,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:28,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:29,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:29,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:30,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:31,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:32,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:33,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:33,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:21:35,516][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:30, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:21:41,705][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:21:41,708][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:21:43,206][__main__][INFO] - Iteration 202 took 1m 4s (32.55% Gen, 67.45% Train). Generation: 20s, Training: 43s. Estimated remaining time: 14h 35m 33s. Estimated total time: 17h 46m 45s. Time estimates for 10 more iterations: 10m 40s, 100 more iterations: 1h 46m 40s, 500 more iterations: 8h 53m 22s. +[2025-08-20 11:21:43,208][__main__][INFO] - Starting iteration 202. +[2025-08-20 11:22:06,635][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:22:06,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:22:06,642][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:22:09,094][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:22:09,096][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:22:09,102][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:22:09,104][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:22:09,105][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:22:09,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:10,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:10,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:11,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:12,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:13,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:14,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:14,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:15,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:16,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:17,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:18,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:18,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:19,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:20,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:21,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:22,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:22,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:23,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:24,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:25,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:26,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:27,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:28,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:29,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:29,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:30,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:31,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:32,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:32,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:33,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:34,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:22:36,176][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:22:37,130][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:22:37,132][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:22:38,479][__main__][INFO] - Iteration 203 took 55s (37.91% Gen, 62.09% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 9m 3s. Estimated total time: 15h 21m 10s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 7s, 500 more iterations: 7h 40m 35s. +[2025-08-20 11:22:38,480][__main__][INFO] - Starting iteration 203. +[2025-08-20 11:23:02,678][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:02,680][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:02,686][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:05,151][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:05,153][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:05,159][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:05,161][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:23:05,161][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:23:05,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:06,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:07,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:07,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:08,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:09,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:10,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:11,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:11,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:12,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:13,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:14,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:14,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:15,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:16,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:17,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:18,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:18,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:19,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:20,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:21,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:22,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:22,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:23,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:24,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:25,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:26,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:26,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:27,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:28,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:29,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:30,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:23:32,128][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:23:33,040][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:23:33,042][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:23:34,407][__main__][INFO] - Iteration 204 took 55s (38.86% Gen, 61.14% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 18m 58s. Estimated total time: 15h 32m 1s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 12s, 500 more iterations: 7h 46m 0s. +[2025-08-20 11:23:34,408][__main__][INFO] - Starting iteration 204. +[2025-08-20 11:23:57,807][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:57,808][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:23:57,815][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:00,292][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:00,293][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:00,300][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:00,302][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:24:00,303][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:24:00,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:01,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:02,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:02,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:03,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:04,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:05,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:06,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:06,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:07,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:08,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:09,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:10,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:10,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:11,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:12,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:13,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:14,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:14,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:15,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:16,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:17,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:18,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:19,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:20,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:20,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:21,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:22,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:23,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:24,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:24,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:25,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:27,325][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:24:28,300][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:24:28,302][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:24:29,617][__main__][INFO] - Iteration 205 took 55s (37.91% Gen, 62.09% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 6m 10s. Estimated total time: 15h 20m 8s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 4s. +[2025-08-20 11:24:29,619][__main__][INFO] - Starting iteration 205. +[2025-08-20 11:24:53,662][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:53,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:53,669][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:56,133][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:56,134][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:56,140][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:24:56,143][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:24:56,143][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:24:56,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:57,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:58,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:58,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:24:59,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:00,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:01,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:01,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:02,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:03,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:04,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:05,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:05,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:06,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:07,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:08,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:09,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:09,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:10,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:11,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:12,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:13,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:14,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:15,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:15,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:16,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:17,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:18,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:19,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:19,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:20,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:21,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:23,198][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:25:24,184][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:25:24,186][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:25:25,514][__main__][INFO] - Iteration 206 took 55s (38.66% Gen, 61.34% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 16m 40s. Estimated total time: 15h 31m 35s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 9s, 500 more iterations: 7h 45m 47s. +[2025-08-20 11:25:25,516][__main__][INFO] - Starting iteration 206. +[2025-08-20 11:25:49,679][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:25:49,680][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:25:49,687][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:25:52,125][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:25:52,126][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:25:52,133][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:25:52,134][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:25:52,135][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:25:52,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:53,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:54,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:54,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:55,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:56,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:57,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:57,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:58,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:25:59,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:00,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:01,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:01,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:02,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:03,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:04,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:05,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:05,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:06,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:07,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:08,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:09,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:09,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:10,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:11,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:12,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:13,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:14,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:15,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:15,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:16,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:17,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:19,122][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:26:20,196][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:26:20,198][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:26:21,630][__main__][INFO] - Iteration 207 took 56s (38.70% Gen, 61.30% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 19m 23s. Estimated total time: 15h 35m 13s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 31s, 500 more iterations: 7h 47m 36s. +[2025-08-20 11:26:21,631][__main__][INFO] - Starting iteration 207. +[2025-08-20 11:26:44,878][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:26:44,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:26:44,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:26:47,336][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:26:47,337][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:26:47,344][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:26:47,346][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:26:47,346][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:26:47,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:48,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:49,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:50,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:50,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:51,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:52,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:53,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:53,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:54,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:55,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:56,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:57,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:57,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:58,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:26:59,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:00,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:01,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:02,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:03,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:03,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:04,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:05,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:06,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:07,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:07,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:08,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:09,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:10,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:11,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:11,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:12,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:14,389][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:27:15,407][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:27:15,409][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:27:16,762][__main__][INFO] - Iteration 208 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12h 2m 4s. Estimated total time: 15h 18m 49s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 24s. +[2025-08-20 11:27:16,763][__main__][INFO] - Starting iteration 208. +[2025-08-20 11:27:40,370][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:27:40,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:27:40,378][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:27:42,834][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:27:42,836][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:27:42,842][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:27:42,844][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:27:42,845][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:27:43,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:43,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:44,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:45,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:46,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:47,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:47,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:48,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:49,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:50,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:51,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:51,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:52,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:53,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:54,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:55,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:55,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:56,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:57,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:58,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:58,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:27:59,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:00,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:01,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:02,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:03,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:04,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:05,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:05,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:06,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:07,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:08,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:09,824][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:28:10,789][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:28:10,791][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:28:12,108][__main__][INFO] - Iteration 209 took 55s (38.23% Gen, 61.77% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 4m 43s. Estimated total time: 15h 22m 24s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 12s. +[2025-08-20 11:28:12,110][__main__][INFO] - Starting iteration 209. +[2025-08-20 11:28:35,394][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:28:35,395][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:28:35,402][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:28:37,877][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:28:37,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:28:37,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:28:37,887][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:28:37,888][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:28:38,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:38,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:39,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:40,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:41,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:42,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:42,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:43,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:44,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:45,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:46,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:46,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:47,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:48,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:49,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:50,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:50,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:51,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:52,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:53,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:54,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:55,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:56,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:56,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:57,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:58,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:28:59,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:00,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:00,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:01,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:02,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:03,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:04,903][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:29:05,834][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:29:05,836][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:29:07,180][__main__][INFO] - Iteration 210 took 55s (37.79% Gen, 62.21% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 59m 13s. Estimated total time: 15h 17m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 54s. +[2025-08-20 11:29:07,181][__main__][INFO] - Starting iteration 210. +[2025-08-20 11:29:31,061][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:29:31,062][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:29:31,068][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:29:33,513][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:29:33,514][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:29:33,521][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:29:33,523][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:29:33,523][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:29:33,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:34,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:35,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:36,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:36,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:37,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:38,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:39,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:40,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:40,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:41,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:42,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:43,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:44,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:44,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:45,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:46,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:47,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:48,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:48,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:49,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:50,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:51,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:52,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:53,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:54,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:55,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:55,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:56,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:57,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:58,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:29:59,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:00,627][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:30:01,571][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:30:01,572][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:30:03,243][__main__][INFO] - Iteration 211 took 56s (38.21% Gen, 61.79% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 14m 49s. Estimated total time: 15h 34m 21s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 26s, 500 more iterations: 7h 47m 10s. +[2025-08-20 11:30:03,245][__main__][INFO] - Starting iteration 211. +[2025-08-20 11:30:26,527][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:30:26,529][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:30:26,536][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:30:29,000][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:30:29,001][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:30:29,008][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:30:29,010][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:30:29,011][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:30:29,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:30,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:30,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:31,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:32,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:33,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:34,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:34,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:35,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:36,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:37,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:38,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:38,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:39,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:40,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:41,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:42,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:42,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:43,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:44,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:45,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:46,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:46,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:48,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:48,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:49,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:50,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:51,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:52,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:52,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:53,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:54,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:30:56,119][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:30:57,084][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:30:57,086][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:30:58,452][__main__][INFO] - Iteration 212 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 59m 39s. Estimated total time: 15h 20m 6s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 3s. +[2025-08-20 11:30:58,453][__main__][INFO] - Starting iteration 212. +[2025-08-20 11:31:22,454][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:31:22,455][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:31:22,462][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:31:24,911][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:31:24,912][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:31:24,919][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:31:24,921][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:31:24,921][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:31:25,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:26,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:26,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:27,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:28,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:29,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:29,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:30,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:31,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:32,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:33,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:33,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:34,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:35,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:36,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:37,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:37,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:38,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:39,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:40,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:41,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:42,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:43,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:44,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:44,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:45,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:46,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:47,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:47,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:48,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:49,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:50,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:31:52,003][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:31:52,972][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:31:52,974][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:31:54,434][__main__][INFO] - Iteration 213 took 55s (38.52% Gen, 61.48% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 11m 37s. Estimated total time: 15h 33m 0s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 18s, 500 more iterations: 7h 46m 30s. +[2025-08-20 11:31:54,436][__main__][INFO] - Starting iteration 213. +[2025-08-20 11:32:20,463][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:32:20,464][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:32:20,471][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:32:22,917][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:32:22,918][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:32:22,924][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:32:22,927][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:32:22,927][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:32:23,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:24,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:24,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:25,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:26,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:27,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:27,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:31,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:32,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:32,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:33,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:34,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:35,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:36,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:37,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:37,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:38,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:39,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:41,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:43,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:43,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:44,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:45,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:46,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:47,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:48,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:49,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:49,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:50,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:51,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:52,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:53,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:32:54,695][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:31, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:32:55,655][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:32:55,657][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:32:57,045][__main__][INFO] - Iteration 214 took 1m 2s (37.69% Gen, 62.31% Train). Generation: 23s, Training: 39s. Estimated remaining time: 14h 1m 2s. Estimated total time: 17h 23m 28s. Time estimates for 10 more iterations: 10m 26s, 100 more iterations: 1h 44m 20s, 500 more iterations: 8h 41m 44s. +[2025-08-20 11:32:57,046][__main__][INFO] - Starting iteration 214. +[2025-08-20 11:33:20,311][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:33:20,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:33:20,319][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:33:22,795][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:33:22,796][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:33:22,802][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:33:22,804][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:33:22,805][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:33:23,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:23,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:24,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:25,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:26,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:27,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:27,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:28,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:29,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:30,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:31,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:31,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:32,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:33,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:34,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:35,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:35,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:36,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:37,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:38,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:38,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:39,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:40,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:41,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:42,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:42,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:43,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:44,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:45,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:46,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:47,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:48,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:33:49,808][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:33:50,753][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:33:50,755][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:33:52,143][__main__][INFO] - Iteration 215 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 54m 54s. Estimated total time: 15h 18m 16s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 8s. +[2025-08-20 11:33:52,144][__main__][INFO] - Starting iteration 215. +[2025-08-20 11:34:15,420][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:34:15,421][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:34:15,427][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:34:17,879][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:34:17,880][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:34:17,886][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:34:17,888][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:34:17,889][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:34:18,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:18,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:19,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:20,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:21,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:22,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:23,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:24,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:25,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:29,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:30,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:30,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:31,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:32,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:33,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:34,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:34,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:35,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:36,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:37,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:38,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:38,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:40,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:40,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:42,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:44,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:45,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:45,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:46,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:47,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:48,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:49,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:34:50,688][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:32, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:34:51,669][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:34:51,671][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:34:53,368][__main__][INFO] - Iteration 216 took 1m 1s (34.00% Gen, 66.00% Train). Generation: 20s, Training: 40s. Estimated remaining time: 13h 36m 0s. Estimated total time: 17h 0m 23s. Time estimates for 10 more iterations: 10m 12s, 100 more iterations: 1h 42m 2s, 500 more iterations: 8h 30m 11s. +[2025-08-20 11:34:53,369][__main__][INFO] - Starting iteration 216. +[2025-08-20 11:35:18,824][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:35:18,826][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:35:18,832][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:35:21,291][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:35:21,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:35:21,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:35:21,301][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:35:21,301][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:35:21,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:22,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:23,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:23,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:24,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:25,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:26,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:27,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:27,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:28,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:29,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:30,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:31,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:31,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:32,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:33,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:34,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:35,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:35,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:36,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:38,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:38,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:39,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:40,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:41,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:41,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:42,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:43,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:44,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:45,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:45,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:46,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:35:48,294][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:35:49,249][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:35:49,251][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:35:52,073][__main__][INFO] - Iteration 217 took 58s (39.17% Gen, 60.83% Train). Generation: 22s, Training: 35s. Estimated remaining time: 12h 53m 2s. Estimated total time: 16h 18m 23s. Time estimates for 10 more iterations: 9m 47s, 100 more iterations: 1h 37m 50s, 500 more iterations: 8h 9m 11s. +[2025-08-20 11:35:52,075][__main__][INFO] - Starting iteration 217. +[2025-08-20 11:36:15,583][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:36:15,584][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:36:15,590][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:36:18,048][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:36:18,049][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:36:18,056][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:36:18,059][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:36:18,060][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:36:18,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:19,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:19,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:20,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:21,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:22,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:23,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:23,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:24,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:25,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:26,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:27,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:27,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:28,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:29,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:30,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:31,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:31,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:32,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:33,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:34,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:35,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:36,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:37,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:37,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:38,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:39,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:40,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:41,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:41,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:42,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:43,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:36:45,094][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:36:46,045][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:36:46,046][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:36:47,396][__main__][INFO] - Iteration 218 took 55s (38.01% Gen, 61.98% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 55m 44s. Estimated total time: 15h 22m 1s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 12s, 500 more iterations: 7h 41m 0s. +[2025-08-20 11:36:47,398][__main__][INFO] - Starting iteration 218. +[2025-08-20 11:37:11,061][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:37:11,062][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:37:11,068][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:37:13,532][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:37:13,533][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:37:13,540][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:37:13,542][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:37:13,542][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:37:13,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:14,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:15,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:16,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:17,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:17,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:18,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:19,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:20,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:20,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:21,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:22,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:23,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:24,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:24,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:25,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:26,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:27,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:28,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:29,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:30,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:30,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:31,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:32,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:33,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:34,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:34,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:35,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:36,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:37,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:38,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:38,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:37:40,473][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:37:41,430][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:37:41,431][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:37:42,779][__main__][INFO] - Iteration 219 took 55s (38.29% Gen, 61.70% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 55m 48s. Estimated total time: 15h 23m 0s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 18s, 500 more iterations: 7h 41m 30s. +[2025-08-20 11:37:42,781][__main__][INFO] - Starting iteration 219. +[2025-08-20 11:38:06,115][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:38:06,116][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:38:06,122][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:38:08,596][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:38:08,597][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:38:08,604][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:38:08,606][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:38:08,606][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:38:08,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:09,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:10,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:11,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:12,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:12,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:13,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:14,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:15,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:16,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:16,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:17,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:18,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:19,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:20,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:20,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:21,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:22,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:23,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:24,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:25,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:26,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:26,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:27,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:28,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:29,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:30,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:30,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:31,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:32,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:33,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:34,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:38:35,663][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:38:36,605][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:38:36,606][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:38:37,969][__main__][INFO] - Iteration 220 took 55s (37.83% Gen, 62.17% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 51m 40s. Estimated total time: 15h 19m 47s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 53s. +[2025-08-20 11:38:37,970][__main__][INFO] - Starting iteration 220. +[2025-08-20 11:39:01,309][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:01,311][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:01,317][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:03,776][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:03,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:03,783][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:03,786][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:39:03,786][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:39:04,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:04,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:05,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:06,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:07,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:08,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:08,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:09,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:10,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:11,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:12,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:12,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:13,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:14,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:15,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:16,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:16,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:17,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:18,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:19,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:19,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:20,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:21,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:22,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:23,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:23,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:24,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:25,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:26,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:27,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:28,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:29,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:39:30,777][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:39:31,875][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:39:31,877][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:39:33,313][__main__][INFO] - Iteration 221 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 53m 19s. Estimated total time: 15h 22m 22s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 11s. +[2025-08-20 11:39:33,314][__main__][INFO] - Starting iteration 221. +[2025-08-20 11:39:56,492][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:56,493][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:56,500][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:58,954][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:58,956][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:58,962][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:39:58,965][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:39:58,966][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:39:59,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:00,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:00,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:01,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:02,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:03,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:04,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:04,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:05,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:06,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:07,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:08,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:08,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:09,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:10,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:11,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:11,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:12,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:13,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:14,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:15,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:15,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:17,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:18,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:18,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:19,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:20,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:21,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:22,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:22,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:23,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:24,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:25,959][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:40:26,922][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:40:26,924][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:40:28,283][__main__][INFO] - Iteration 222 took 54s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 46m 11s. Estimated total time: 15h 16m 8s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 4s. +[2025-08-20 11:40:28,285][__main__][INFO] - Starting iteration 222. +[2025-08-20 11:40:52,456][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:40:52,458][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:40:52,464][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:40:54,945][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:40:54,947][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:40:54,953][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:40:54,956][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:40:54,956][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:40:55,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:56,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:56,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:57,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:58,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:40:59,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:00,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:00,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:01,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:02,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:03,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:03,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:04,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:05,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:06,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:07,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:07,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:08,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:09,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:10,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:11,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:12,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:13,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:14,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:14,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:15,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:16,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:17,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:17,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:18,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:19,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:20,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:21,932][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:41:22,858][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:41:22,860][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:41:24,182][__main__][INFO] - Iteration 223 took 55s (38.82% Gen, 61.18% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 0m 43s. Estimated total time: 15h 31m 36s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 9s, 500 more iterations: 7h 45m 48s. +[2025-08-20 11:41:24,184][__main__][INFO] - Starting iteration 223. +[2025-08-20 11:41:47,823][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:41:47,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:41:47,831][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:41:50,288][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:41:50,290][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:41:50,296][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:41:50,299][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:41:50,299][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:41:50,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:51,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:52,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:52,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:53,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:54,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:55,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:56,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:56,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:57,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:58,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:41:59,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:00,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:00,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:01,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:02,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:03,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:04,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:05,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:06,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:06,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:07,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:08,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:09,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:10,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:10,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:11,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:12,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:13,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:14,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:14,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:15,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:17,200][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:42:18,195][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:42:18,196][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:42:19,517][__main__][INFO] - Iteration 224 took 55s (38.32% Gen, 61.68% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 50m 24s. Estimated total time: 15h 22m 13s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 6s. +[2025-08-20 11:42:19,519][__main__][INFO] - Starting iteration 224. +[2025-08-20 11:42:42,825][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:42:42,826][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:42:42,832][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:42:45,321][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:42:45,323][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:42:45,329][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:42:45,331][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:42:45,332][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:42:45,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:46,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:47,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:48,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:48,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:49,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:50,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:51,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:51,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:52,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:53,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:54,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:55,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:55,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:56,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:57,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:58,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:59,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:42:59,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:00,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:01,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:02,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:03,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:04,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:05,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:05,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:06,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:07,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:08,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:09,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:09,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:10,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:12,299][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:43:13,285][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:43:13,286][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:43:14,585][__main__][INFO] - Iteration 225 took 55s (37.82% Gen, 62.18% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 45m 2s. Estimated total time: 15h 17m 46s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 53s. +[2025-08-20 11:43:14,587][__main__][INFO] - Starting iteration 225. +[2025-08-20 11:43:37,944][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:43:37,948][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:43:37,957][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:43:40,407][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:43:40,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:43:40,415][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:43:40,417][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:43:40,418][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:43:40,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:41,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:42,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:43,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:43,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:44,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:45,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:46,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:47,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:47,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:48,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:49,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:50,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:51,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:51,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:52,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:53,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:54,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:55,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:55,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:56,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:57,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:58,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:43:59,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:00,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:01,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:01,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:02,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:03,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:04,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:05,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:05,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:07,418][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:44:08,380][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:44:08,381][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:44:09,847][__main__][INFO] - Iteration 226 took 55s (37.85% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 47m 21s. Estimated total time: 15h 20m 59s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 5s, 500 more iterations: 7h 40m 29s. +[2025-08-20 11:44:09,849][__main__][INFO] - Starting iteration 226. +[2025-08-20 11:44:33,274][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:44:33,275][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:44:33,282][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:44:35,780][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:44:35,782][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:44:35,788][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:44:35,791][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:44:35,791][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:44:36,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:36,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:37,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:38,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:39,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:40,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:40,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:41,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:42,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:43,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:44,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:44,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:45,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:46,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:47,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:48,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:48,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:49,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:50,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:51,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:51,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:52,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:53,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:54,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:55,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:55,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:56,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:57,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:58,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:44:59,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:00,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:01,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:02,687][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:45:03,732][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:45:03,735][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:45:05,216][__main__][INFO] - Iteration 227 took 55s (37.80% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 48m 12s. Estimated total time: 15h 22m 47s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 23s. +[2025-08-20 11:45:05,218][__main__][INFO] - Starting iteration 227. +[2025-08-20 11:45:28,478][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:45:28,480][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:45:28,486][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:45:30,946][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:45:30,948][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:45:30,954][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:45:30,956][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:45:30,957][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:45:31,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:32,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:32,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:33,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:34,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:35,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:36,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:36,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:37,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:38,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:39,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:39,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:40,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:41,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:42,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:43,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:43,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:44,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:45,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:46,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:47,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:47,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:48,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:50,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:50,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:51,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:52,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:53,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:53,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:54,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:55,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:56,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:45:57,928][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:45:58,860][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:45:58,862][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:46:00,245][__main__][INFO] - Iteration 228 took 55s (37.79% Gen, 62.21% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 41m 37s. Estimated total time: 15h 17m 6s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 33s. +[2025-08-20 11:46:00,247][__main__][INFO] - Starting iteration 228. +[2025-08-20 11:46:23,840][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:46:23,842][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:46:23,848][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:46:26,309][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:46:26,310][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:46:26,317][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:46:26,319][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:46:26,320][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:46:26,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:27,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:28,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:29,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:29,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:30,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:31,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:32,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:32,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:33,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:34,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:35,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:36,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:36,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:37,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:38,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:39,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:40,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:40,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:42,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:43,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:43,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:44,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:45,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:46,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:47,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:47,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:48,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:49,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:50,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:50,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:51,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:46:53,363][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:46:54,295][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:46:54,297][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:46:55,668][__main__][INFO] - Iteration 229 took 55s (38.13% Gen, 61.87% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 47m 15s. Estimated total time: 15h 23m 40s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 22s, 500 more iterations: 7h 41m 50s. +[2025-08-20 11:46:55,669][__main__][INFO] - Starting iteration 229. +[2025-08-20 11:47:18,885][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:47:18,886][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:47:18,892][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:47:21,351][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:47:21,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:47:21,359][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:47:21,361][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:47:21,362][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:47:21,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:22,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:23,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:24,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:24,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:25,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:26,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:27,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:28,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:28,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:29,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:30,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:31,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:31,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:32,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:34,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:34,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:35,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:36,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:37,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:37,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:38,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:39,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:40,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:41,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:41,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:42,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:43,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:44,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:45,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:45,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:46,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:47:48,295][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:47:49,253][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:47:49,255][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:47:50,588][__main__][INFO] - Iteration 230 took 54s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 37m 59s. Estimated total time: 15h 15m 18s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 39s. +[2025-08-20 11:47:50,590][__main__][INFO] - Starting iteration 230. +[2025-08-20 11:48:13,894][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:48:13,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:48:13,901][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:48:16,388][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:48:16,389][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:48:16,396][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:48:16,398][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:48:16,399][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:48:16,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:17,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:18,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:19,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:19,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:20,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:21,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:22,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:23,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:23,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:24,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:25,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:26,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:27,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:27,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:28,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:29,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:30,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:31,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:31,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:32,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:33,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:34,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:35,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:36,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:37,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:37,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:38,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:39,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:40,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:41,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:41,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:48:43,474][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:48:45,804][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:48:45,805][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:48:47,081][__main__][INFO] - Iteration 231 took 56s (36.90% Gen, 63.10% Train). Generation: 20s, Training: 35s. Estimated remaining time: 12h 3m 15s. Estimated total time: 15h 41m 31s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 9s, 500 more iterations: 7h 50m 45s. +[2025-08-20 11:48:48,778][__main__][INFO] - Starting iteration 231. +[2025-08-20 11:49:12,214][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:49:12,215][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:49:12,221][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:49:14,684][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:49:14,685][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:49:14,692][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:49:14,694][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:49:14,695][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:49:14,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:15,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:16,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:17,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:18,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:18,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:19,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:20,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:21,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:22,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:24,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:25,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:26,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:27,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:28,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:28,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:29,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:30,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:31,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:32,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:32,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:33,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:34,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:35,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:36,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:37,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:38,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:38,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:39,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:40,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:41,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:42,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:49:43,601][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:49:44,544][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:49:44,546][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:49:45,944][__main__][INFO] - Iteration 232 took 57s (36.69% Gen, 63.30% Train). Generation: 20s, Training: 36s. Estimated remaining time: 12h 13m 20s. Estimated total time: 15h 52m 35s. Time estimates for 10 more iterations: 9m 31s, 100 more iterations: 1h 35m 15s, 500 more iterations: 7h 56m 17s. +[2025-08-20 11:49:45,947][__main__][INFO] - Starting iteration 232. +[2025-08-20 11:50:09,231][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:50:09,232][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:50:09,239][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:50:11,733][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:50:11,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:50:11,741][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:50:11,743][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:50:11,744][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:50:12,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:12,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:13,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:14,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:15,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:16,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:16,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:17,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:18,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:19,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:19,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:20,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:21,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:22,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:23,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:23,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:24,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:25,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:26,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:27,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:27,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:29,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:29,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:30,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:31,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:32,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:33,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:33,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:34,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:35,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:36,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:37,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:50:38,660][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:50:39,709][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:50:39,711][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:50:41,172][__main__][INFO] - Iteration 233 took 55s (37.65% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 40m 12s. Estimated total time: 15h 20m 22s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 11s. +[2025-08-20 11:50:41,174][__main__][INFO] - Starting iteration 233. +[2025-08-20 11:51:04,524][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:04,526][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:04,532][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:06,988][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:06,989][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:06,995][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:06,997][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:51:06,998][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:51:07,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:08,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:08,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:09,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:10,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:11,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:12,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:12,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:13,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:14,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:15,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:16,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:16,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:17,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:18,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:19,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:19,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:20,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:22,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:22,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:23,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:24,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:25,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:25,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:26,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:27,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:28,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:29,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:29,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:30,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:31,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:32,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:51:33,973][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:51:34,934][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:51:34,936][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:51:36,298][__main__][INFO] - Iteration 234 took 55s (37.91% Gen, 62.09% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 37m 38s. Estimated total time: 15h 18m 44s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 22s. +[2025-08-20 11:51:36,313][__main__][INFO] - Starting iteration 234. +[2025-08-20 11:51:59,916][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:59,918][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:51:59,924][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:02,377][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:02,378][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:02,384][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:02,387][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:52:02,387][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:52:02,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:03,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:04,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:05,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:05,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:06,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:07,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:08,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:09,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:09,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:10,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:11,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:12,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:12,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:13,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:14,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:15,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:16,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:16,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:17,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:18,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:19,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:20,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:21,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:22,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:22,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:23,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:24,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:25,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:26,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:26,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:27,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:29,325][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:52:30,248][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:52:30,250][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:52:31,605][__main__][INFO] - Iteration 235 took 55s (38.23% Gen, 61.77% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 39m 30s. Estimated total time: 15h 21m 30s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 9s, 500 more iterations: 7h 40m 45s. +[2025-08-20 11:52:31,607][__main__][INFO] - Starting iteration 235. +[2025-08-20 11:52:55,004][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:55,005][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:55,012][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:57,474][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:57,475][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:57,482][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:52:57,484][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:52:57,484][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:52:57,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:58,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:52:59,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:00,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:00,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:01,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:02,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:03,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:04,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:04,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:05,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:06,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:07,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:08,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:08,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:09,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:10,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:11,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:12,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:12,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:13,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:14,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:15,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:16,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:16,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:18,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:18,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:19,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:20,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:21,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:22,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:22,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:24,444][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:53:25,409][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:53:25,411][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:53:26,788][__main__][INFO] - Iteration 236 took 55s (37.95% Gen, 62.05% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 36m 45s. Estimated total time: 15h 19m 40s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 50s. +[2025-08-20 11:53:26,790][__main__][INFO] - Starting iteration 236. +[2025-08-20 11:53:50,180][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:53:50,181][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:53:50,188][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:53:52,642][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:53:52,643][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:53:52,650][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:53:52,652][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:53:52,652][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:53:52,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:53,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:54,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:55,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:56,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:56,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:57,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:58,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:53:59,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:00,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:00,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:01,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:02,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:03,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:04,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:04,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:05,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:06,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:07,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:08,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:08,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:09,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:10,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:11,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:12,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:13,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:14,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:15,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:15,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:16,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:17,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:18,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:19,924][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:54:20,864][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:54:20,865][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:54:22,496][__main__][INFO] - Iteration 237 took 55s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 44m 33s. Estimated total time: 15h 28m 25s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 50s, 500 more iterations: 7h 44m 12s. +[2025-08-20 11:54:22,497][__main__][INFO] - Starting iteration 237. +[2025-08-20 11:54:45,815][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:54:45,816][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:54:45,822][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:54:48,277][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:54:48,278][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:54:48,284][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:54:48,286][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:54:48,287][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:54:48,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:49,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:50,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:50,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:51,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:52,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:53,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:54,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:54,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:55,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:56,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:57,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:58,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:58,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:54:59,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:00,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:01,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:02,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:03,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:04,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:04,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:05,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:06,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:07,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:08,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:08,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:09,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:10,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:11,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:12,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:12,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:13,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:15,225][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:55:16,243][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:55:16,245][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:55:17,610][__main__][INFO] - Iteration 238 took 55s (37.83% Gen, 62.17% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 33m 46s. Estimated total time: 15h 18m 32s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 51s, 500 more iterations: 7h 39m 16s. +[2025-08-20 11:55:17,612][__main__][INFO] - Starting iteration 238. +[2025-08-20 11:55:40,970][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:55:40,971][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:55:40,977][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:55:43,447][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:55:43,448][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:55:43,454][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:55:43,457][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:55:43,457][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:55:43,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:44,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:45,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:46,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:46,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:47,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:48,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:49,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:50,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:50,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:51,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:52,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:53,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:54,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:54,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:55,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:56,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:57,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:58,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:58,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:55:59,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:00,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:01,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:02,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:03,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:04,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:04,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:05,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:06,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:07,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:08,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:08,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:10,406][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:56:11,341][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:56:11,343][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:56:12,815][__main__][INFO] - Iteration 239 took 55s (37.85% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 34m 21s. Estimated total time: 15h 20m 2s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 1s. +[2025-08-20 11:56:12,817][__main__][INFO] - Starting iteration 239. +[2025-08-20 11:56:37,694][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:56:37,695][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:56:37,701][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:56:40,181][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:56:40,182][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:56:40,189][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:56:40,191][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:56:40,191][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:56:40,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:41,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:42,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:42,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:43,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:44,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:45,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:46,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:46,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:47,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:48,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:49,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:50,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:50,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:51,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:52,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:53,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:53,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:54,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:55,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:56,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:57,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:57,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:56:59,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:00,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:00,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:01,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:02,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:03,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:04,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:04,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:05,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:07,198][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:57:08,147][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:57:08,149][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:57:09,604][__main__][INFO] - Iteration 240 took 56s (39.43% Gen, 60.57% Train). Generation: 22s, Training: 34s. Estimated remaining time: 11h 59m 48s. Estimated total time: 15h 46m 26s. Time estimates for 10 more iterations: 9m 27s, 100 more iterations: 1h 34m 38s, 500 more iterations: 7h 53m 13s. +[2025-08-20 11:57:09,605][__main__][INFO] - Starting iteration 240. +[2025-08-20 11:57:32,983][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:57:32,985][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:57:32,991][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:57:35,461][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:57:35,462][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:57:35,469][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:57:35,472][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:57:35,472][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:57:35,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:36,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:37,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:38,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:38,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:39,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:40,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:41,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:42,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:42,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:43,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:44,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:45,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:46,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:46,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:47,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:48,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:49,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:50,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:50,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:51,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:52,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:53,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:54,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:55,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:56,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:56,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:57,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:58,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:57:59,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:00,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:00,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:02,428][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:58:03,526][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:58:03,557][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:58:04,939][__main__][INFO] - Iteration 241 took 55s (37.77% Gen, 62.22% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 34m 39s. Estimated total time: 15h 22m 13s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 6s. +[2025-08-20 11:58:04,941][__main__][INFO] - Starting iteration 241. +[2025-08-20 11:58:28,280][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:58:28,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:58:28,288][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:58:30,744][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:58:30,746][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:58:30,752][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:58:30,755][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:58:30,755][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:58:31,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:31,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:32,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:33,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:34,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:35,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:35,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:36,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:37,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:38,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:38,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:39,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:40,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:41,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:42,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:42,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:43,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:44,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:45,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:46,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:46,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:47,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:48,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:49,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:50,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:51,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:52,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:52,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:53,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:54,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:55,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:56,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:58:57,702][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:58:58,658][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:58:58,659][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:59:00,121][__main__][INFO] - Iteration 242 took 55s (37.83% Gen, 62.17% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 31m 10s. Estimated total time: 15h 19m 39s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 49s. +[2025-08-20 11:59:00,123][__main__][INFO] - Starting iteration 242. +[2025-08-20 11:59:23,342][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:59:23,344][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:59:23,350][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:59:25,817][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:59:25,818][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:59:25,825][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 11:59:25,827][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 11:59:25,828][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 11:59:26,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:26,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:27,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:28,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:29,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:30,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:30,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:31,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:32,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:33,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:34,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:34,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:35,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:36,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:37,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:38,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:38,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:39,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:40,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:41,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:42,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:43,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:44,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:44,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:45,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:46,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:47,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:48,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:48,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:49,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:50,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:51,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 11:59:52,816][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 11:59:53,924][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 11:59:53,927][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 11:59:55,659][__main__][INFO] - Iteration 243 took 55s (37.41% Gen, 62.59% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 36m 11s. Estimated total time: 15h 25m 36s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 33s, 500 more iterations: 7h 42m 48s. +[2025-08-20 11:59:55,661][__main__][INFO] - Starting iteration 243. +[2025-08-20 12:00:19,582][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:00:19,584][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:00:19,590][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:00:22,045][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:00:22,046][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:00:22,053][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:00:22,055][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:00:22,056][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:00:22,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:23,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:23,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:24,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:25,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:26,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:27,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:27,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:28,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:29,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:30,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:31,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:31,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:32,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:33,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:34,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:35,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:35,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:36,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:37,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:38,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:39,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:39,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:40,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:41,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:42,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:43,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:43,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:45,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:45,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:46,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:47,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:00:49,004][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:00:49,955][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:00:49,957][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:00:51,298][__main__][INFO] - Iteration 244 took 55s (38.58% Gen, 61.42% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 36m 56s. Estimated total time: 15h 27m 16s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 43s, 500 more iterations: 7h 43m 38s. +[2025-08-20 12:00:51,299][__main__][INFO] - Starting iteration 244. +[2025-08-20 12:01:14,611][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:01:14,612][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:01:14,618][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:01:17,076][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:01:17,077][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:01:17,084][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:01:17,086][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:01:17,087][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:01:17,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:18,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:18,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:19,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:20,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:21,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:22,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:22,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:23,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:24,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:25,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:26,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:26,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:27,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:28,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:29,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:30,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:30,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:31,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:32,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:33,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:34,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:34,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:36,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:36,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:37,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:38,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:39,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:40,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:40,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:41,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:42,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:01:44,023][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:01:44,961][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:01:44,963][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:01:46,353][__main__][INFO] - Iteration 245 took 55s (37.87% Gen, 62.12% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 26m 18s. Estimated total time: 15h 17m 34s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 47s. +[2025-08-20 12:01:46,355][__main__][INFO] - Starting iteration 245. +[2025-08-20 12:02:09,649][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:02:09,650][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:02:09,657][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:02:12,109][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:02:12,110][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:02:12,117][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:02:12,119][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:02:12,119][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:02:12,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:13,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:14,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:14,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:15,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:16,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:17,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:17,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:18,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:19,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:20,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:21,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:21,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:22,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:23,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:24,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:25,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:25,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:26,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:27,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:28,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:29,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:30,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:31,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:31,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:32,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:33,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:34,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:35,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:35,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:36,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:37,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:02:39,091][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:02:40,046][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:02:40,048][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:02:41,623][__main__][INFO] - Iteration 246 took 55s (37.67% Gen, 62.33% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 28m 57s. Estimated total time: 15h 21m 7s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 33s. +[2025-08-20 12:02:41,625][__main__][INFO] - Starting iteration 246. +[2025-08-20 12:03:05,375][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:03:05,376][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:03:05,382][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:03:07,833][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:03:07,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:03:07,841][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:03:07,844][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:03:07,844][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:03:08,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:08,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:09,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:10,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:11,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:12,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:12,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:13,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:14,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:15,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:16,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:16,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:17,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:18,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:19,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:20,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:20,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:21,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:22,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:23,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:24,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:24,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:25,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:26,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:27,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:28,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:29,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:30,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:30,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:31,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:32,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:33,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:03:34,816][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:03:35,774][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:03:35,776][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:03:37,539][__main__][INFO] - Iteration 247 took 55s (38.07% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 38m 47s. Estimated total time: 15h 31m 53s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 11s, 500 more iterations: 7h 45m 56s. +[2025-08-20 12:03:37,540][__main__][INFO] - Starting iteration 247. +[2025-08-20 12:04:00,763][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:00,765][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:00,771][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:03,212][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:03,213][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:03,219][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:03,222][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:04:03,222][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:04:03,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:04,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:05,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:05,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:06,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:07,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:08,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:09,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:09,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:10,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:11,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:12,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:13,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:13,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:14,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:15,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:16,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:17,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:18,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:19,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:19,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:20,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:21,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:22,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:23,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:23,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:24,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:25,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:26,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:26,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:27,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:28,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:30,150][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:04:31,094][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:04:31,095][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:04:33,117][__main__][INFO] - Iteration 248 took 55s (37.41% Gen, 62.59% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 32m 14s. Estimated total time: 15h 26m 16s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 37s, 500 more iterations: 7h 43m 8s. +[2025-08-20 12:04:33,119][__main__][INFO] - Starting iteration 248. +[2025-08-20 12:04:56,355][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:56,356][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:56,363][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:58,839][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:58,841][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:58,847][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:04:58,849][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:04:58,850][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:04:59,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:04:59,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:00,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:01,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:02,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:03,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:03,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:04,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:05,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:06,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:07,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:07,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:08,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:09,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:10,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:11,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:11,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:12,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:13,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:14,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:15,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:16,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:17,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:17,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:18,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:19,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:20,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:21,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:21,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:22,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:23,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:24,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:25,826][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:05:26,790][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:05:26,792][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:05:28,156][__main__][INFO] - Iteration 249 took 55s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 22m 19s. Estimated total time: 15h 17m 16s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 38s. +[2025-08-20 12:05:28,158][__main__][INFO] - Starting iteration 249. +[2025-08-20 12:05:51,810][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:05:51,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:05:51,817][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:05:54,250][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:05:54,251][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:05:54,258][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:05:54,260][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:05:54,261][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:05:54,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:55,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:56,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:56,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:57,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:58,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:05:59,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:00,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:00,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:01,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:02,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:03,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:04,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:04,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:05,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:06,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:07,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:08,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:09,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:10,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:10,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:11,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:12,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:13,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:14,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:14,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:15,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:16,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:17,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:18,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:18,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:19,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:21,173][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:06:22,165][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:06:22,166][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:06:23,526][__main__][INFO] - Iteration 250 took 55s (38.34% Gen, 61.66% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 26m 54s. Estimated total time: 15h 22m 47s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 23s. +[2025-08-20 12:06:23,527][__main__][INFO] - Starting iteration 250. +[2025-08-20 12:06:47,168][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:06:47,169][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:06:47,176][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:06:49,648][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:06:49,649][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:06:49,655][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:06:49,658][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:06:49,658][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:06:49,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:50,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:51,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:52,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:53,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:53,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:54,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:55,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:56,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:57,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:57,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:58,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:06:59,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:00,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:01,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:01,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:02,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:03,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:04,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:05,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:05,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:07,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:07,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:08,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:09,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:10,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:11,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:11,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:12,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:13,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:14,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:15,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:16,655][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:07:17,606][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:07:17,607][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:07:21,609][__main__][INFO] - Iteration 251 took 58s (36.48% Gen, 59.01% Train). Generation: 21s, Training: 34s. Estimated remaining time: 12h 11m 9s. Estimated total time: 16h 8m 0s. Time estimates for 10 more iterations: 9m 40s, 100 more iterations: 1h 36m 48s, 500 more iterations: 8h 4m 0s. +[2025-08-20 12:07:21,610][__main__][INFO] - Starting iteration 251. +[2025-08-20 12:07:44,844][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:07:44,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:07:44,851][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:07:47,347][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:07:47,348][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:07:47,355][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:07:47,357][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:07:47,358][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:07:47,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:48,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:49,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:50,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:50,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:51,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:52,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:53,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:54,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:54,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:55,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:56,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:57,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:57,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:58,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:07:59,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:00,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:01,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:01,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:02,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:03,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:04,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:05,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:05,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:07,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:08,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:08,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:09,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:10,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:11,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:11,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:12,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:14,342][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:08:15,285][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:08:15,286][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:08:16,618][__main__][INFO] - Iteration 252 took 55s (37.74% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 19m 2s. Estimated total time: 15h 16m 47s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 23s. +[2025-08-20 12:08:16,620][__main__][INFO] - Starting iteration 252. +[2025-08-20 12:08:39,805][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:08:39,807][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:08:39,813][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:08:42,268][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:08:42,270][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:08:42,276][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:08:42,278][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:08:42,279][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:08:42,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:43,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:44,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:44,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:45,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:46,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:47,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:48,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:48,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:49,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:50,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:51,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:52,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:52,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:53,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:54,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:55,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:56,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:56,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:57,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:58,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:08:59,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:00,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:01,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:02,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:02,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:03,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:04,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:05,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:05,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:06,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:07,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:09,131][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:09:10,075][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:09:10,077][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:09:11,595][__main__][INFO] - Iteration 253 took 54s (37.74% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 17m 34s. Estimated total time: 15h 16m 15s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 7s. +[2025-08-20 12:09:11,597][__main__][INFO] - Starting iteration 253. +[2025-08-20 12:09:34,958][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:09:34,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:09:34,966][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:09:37,419][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:09:37,420][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:09:37,427][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:09:37,430][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:09:37,430][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:09:37,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:38,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:39,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:40,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:40,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:41,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:42,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:43,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:44,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:44,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:45,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:46,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:47,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:48,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:48,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:49,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:50,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:51,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:52,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:52,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:53,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:54,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:55,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:56,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:57,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:58,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:59,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:09:59,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:00,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:01,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:02,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:03,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:04,619][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:10:05,586][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:10:05,587][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:10:07,060][__main__][INFO] - Iteration 254 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 24m 46s. Estimated total time: 15h 24m 22s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 26s, 500 more iterations: 7h 42m 11s. +[2025-08-20 12:10:07,062][__main__][INFO] - Starting iteration 254. +[2025-08-20 12:10:30,657][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:10:30,688][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:10:30,707][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:10:33,163][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:10:33,164][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:10:33,170][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:10:33,173][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:10:33,173][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:10:33,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:34,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:35,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:35,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:36,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:37,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:38,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:39,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:39,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:40,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:41,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:42,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:43,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:43,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:44,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:45,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:46,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:46,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:47,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:48,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:49,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:50,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:51,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:52,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:53,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:53,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:54,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:55,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:56,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:57,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:57,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:10:58,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:00,266][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:11:01,233][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:11:01,234][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:11:02,631][__main__][INFO] - Iteration 255 took 55s (38.04% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 25m 37s. Estimated total time: 15h 26m 8s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 36s, 500 more iterations: 7h 43m 4s. +[2025-08-20 12:11:02,633][__main__][INFO] - Starting iteration 255. +[2025-08-20 12:11:26,019][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:11:26,021][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:11:26,027][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:11:28,500][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:11:28,501][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:11:28,508][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:11:28,510][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:11:28,511][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:11:28,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:29,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:30,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:31,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:31,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:32,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:33,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:34,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:35,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:35,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:36,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:37,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:38,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:39,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:39,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:40,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:41,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:42,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:43,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:43,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:44,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:45,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:46,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:47,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:48,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:49,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:49,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:50,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:51,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:52,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:53,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:53,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:11:55,416][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:11:56,384][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:11:56,387][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:11:58,327][__main__][INFO] - Iteration 256 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 26m 41s. Estimated total time: 15h 28m 8s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 48s, 500 more iterations: 7h 44m 4s. +[2025-08-20 12:11:58,329][__main__][INFO] - Starting iteration 256. +[2025-08-20 12:12:21,706][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:12:21,707][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:12:21,713][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:12:24,174][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:12:24,175][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:12:24,181][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:12:24,184][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:12:24,184][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:12:24,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:25,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:26,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:26,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:27,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:28,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:29,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:30,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:30,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:31,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:32,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:33,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:34,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:34,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:35,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:36,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:37,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:38,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:39,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:40,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:40,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:41,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:42,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:43,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:44,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:44,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:45,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:46,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:47,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:47,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:48,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:49,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:12:51,176][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:12:52,179][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:12:52,181][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:12:53,554][__main__][INFO] - Iteration 257 took 55s (37.88% Gen, 62.12% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 18m 2s. Estimated total time: 15h 20m 24s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 12s. +[2025-08-20 12:12:53,558][__main__][INFO] - Starting iteration 257. +[2025-08-20 12:13:18,566][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:13:18,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:13:18,575][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:13:21,044][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:13:21,045][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:13:21,052][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:13:21,054][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:13:21,054][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:13:21,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:22,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:22,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:23,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:24,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:25,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:26,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:26,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:27,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:28,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:29,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:30,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:30,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:31,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:32,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:33,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:34,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:34,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:36,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:36,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:37,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:38,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:39,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:40,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:40,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:41,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:42,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:43,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:44,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:44,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:45,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:46,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:13:48,065][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:13:49,036][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:13:49,038][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:13:50,402][__main__][INFO] - Iteration 258 took 56s (39.62% Gen, 60.38% Train). Generation: 22s, Training: 34s. Estimated remaining time: 11h 44m 4s. Estimated total time: 15h 47m 24s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 44s, 500 more iterations: 7h 53m 42s. +[2025-08-20 12:13:50,405][__main__][INFO] - Starting iteration 258. +[2025-08-20 12:14:13,725][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:14:13,726][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:14:13,732][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:14:16,169][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:14:16,170][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:14:16,177][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:14:16,179][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:14:16,180][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:14:16,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:17,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:18,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:18,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:19,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:20,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:21,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:22,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:22,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:23,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:24,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:25,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:25,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:26,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:27,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:28,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:29,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:29,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:30,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:31,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:32,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:33,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:34,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:35,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:36,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:36,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:37,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:38,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:39,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:39,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:40,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:41,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:14:43,125][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:14:44,220][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:14:44,222][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:14:45,719][__main__][INFO] - Iteration 259 took 55s (37.71% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 17m 39s. Estimated total time: 15h 21m 53s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 56s. +[2025-08-20 12:14:45,721][__main__][INFO] - Starting iteration 259. +[2025-08-20 12:15:09,089][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:15:09,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:15:09,097][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:15:11,563][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:15:11,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:15:11,571][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:15:11,574][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:15:11,574][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:15:11,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:12,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:13,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:14,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:15,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:15,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:16,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:17,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:18,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:19,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:19,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:20,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:21,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:22,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:22,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:23,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:24,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:25,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:26,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:27,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:28,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:29,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:29,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:30,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:31,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:32,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:32,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:33,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:34,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:35,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:36,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:36,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:15:38,518][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:15:39,552][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:15:39,554][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:15:41,264][__main__][INFO] - Iteration 260 took 55s (37.64% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 20m 32s. Estimated total time: 15h 25m 42s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 34s, 500 more iterations: 7h 42m 51s. +[2025-08-20 12:15:41,266][__main__][INFO] - Starting iteration 260. +[2025-08-20 12:16:04,875][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:16:04,877][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:16:04,883][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:16:07,313][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:16:07,315][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:16:07,322][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:16:07,324][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:16:07,324][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:16:07,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:08,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:09,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:09,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:10,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:11,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:12,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:13,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:13,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:14,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:15,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:16,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:17,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:17,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:18,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:19,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:20,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:21,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:22,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:23,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:23,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:24,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:25,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:26,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:27,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:27,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:28,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:29,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:30,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:31,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:31,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:32,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:16:34,298][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:16:35,256][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:16:35,257][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:16:36,645][__main__][INFO] - Iteration 261 took 55s (38.24% Gen, 61.75% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 16m 53s. Estimated total time: 15h 22m 58s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 17s, 500 more iterations: 7h 41m 29s. +[2025-08-20 12:16:36,647][__main__][INFO] - Starting iteration 261. +[2025-08-20 12:17:00,073][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:00,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:00,081][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:02,550][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:02,552][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:02,558][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:02,561][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:17:02,561][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:17:02,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:03,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:04,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:05,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:06,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:06,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:07,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:08,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:09,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:09,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:10,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:11,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:12,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:13,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:13,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:14,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:15,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:16,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:17,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:17,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:18,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:19,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:20,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:21,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:22,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:23,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:23,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:24,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:25,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:26,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:27,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:27,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:29,478][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:17:30,448][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:17:30,450][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:17:31,829][__main__][INFO] - Iteration 262 took 55s (37.97% Gen, 62.02% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 12m 41s. Estimated total time: 15h 19m 41s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 50s. +[2025-08-20 12:17:31,830][__main__][INFO] - Starting iteration 262. +[2025-08-20 12:17:55,057][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:55,058][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:55,065][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:57,515][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:57,516][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:57,523][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:17:57,525][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:17:57,525][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:17:57,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:58,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:17:59,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:00,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:00,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:01,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:02,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:03,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:04,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:04,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:05,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:06,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:07,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:08,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:08,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:09,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:10,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:11,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:12,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:13,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:14,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:14,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:15,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:16,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:17,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:18,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:18,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:19,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:20,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:21,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:22,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:22,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:24,489][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:18:25,419][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:18:25,427][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:18:28,363][__main__][INFO] - Iteration 263 took 56s (36.76% Gen, 63.24% Train). Generation: 20s, Training: 35s. Estimated remaining time: 11h 34m 14s. Estimated total time: 15h 42m 11s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 13s, 500 more iterations: 7h 51m 5s. +[2025-08-20 12:18:28,364][__main__][INFO] - Starting iteration 263. +[2025-08-20 12:18:52,032][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:18:52,033][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:18:52,040][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:18:54,517][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:18:54,519][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:18:54,525][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:18:54,527][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:18:54,528][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:18:54,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:55,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:56,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:57,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:57,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:58,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:18:59,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:00,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:01,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:01,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:02,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:03,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:04,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:05,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:05,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:06,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:07,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:08,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:09,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:09,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:11,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:11,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:12,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:13,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:14,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:15,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:15,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:16,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:17,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:18,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:19,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:19,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:21,496][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:19:22,471][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:19:22,472][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:19:24,361][__main__][INFO] - Iteration 264 took 55s (37.86% Gen, 62.14% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 24m 23s. Estimated total time: 15h 33m 16s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 19s, 500 more iterations: 7h 46m 38s. +[2025-08-20 12:19:24,363][__main__][INFO] - Starting iteration 264. +[2025-08-20 12:19:48,349][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:19:48,350][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:19:48,357][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:19:50,785][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:19:50,786][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:19:50,793][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:19:50,795][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:19:50,795][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:19:51,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:51,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:52,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:53,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:54,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:55,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:55,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:56,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:57,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:58,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:59,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:19:59,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:00,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:01,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:02,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:03,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:03,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:04,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:05,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:06,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:07,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:07,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:08,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:09,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:10,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:11,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:12,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:13,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:13,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:14,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:15,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:16,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:17,796][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:20:18,852][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:20:18,854][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:20:20,427][__main__][INFO] - Iteration 265 took 56s (38.43% Gen, 61.57% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 24m 33s. Estimated total time: 15h 34m 23s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 26s, 500 more iterations: 7h 47m 11s. +[2025-08-20 12:20:20,429][__main__][INFO] - Starting iteration 265. +[2025-08-20 12:20:44,774][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:20:44,775][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:20:44,781][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:20:47,195][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:20:47,197][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:20:47,203][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:20:47,205][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:20:47,205][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:20:47,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:48,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:49,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:49,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:50,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:51,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:52,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:53,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:53,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:54,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:55,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:56,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:57,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:57,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:58,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:20:59,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:00,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:01,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:01,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:02,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:03,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:04,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:05,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:06,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:07,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:07,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:08,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:09,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:10,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:11,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:11,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:12,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:14,193][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:21:15,227][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:21:15,230][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:21:16,697][__main__][INFO] - Iteration 266 took 56s (38.94% Gen, 61.06% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 27m 2s. Estimated total time: 15h 37m 48s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 46s, 500 more iterations: 7h 48m 54s. +[2025-08-20 12:21:16,699][__main__][INFO] - Starting iteration 266. +[2025-08-20 12:21:40,111][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:21:40,112][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:21:40,118][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:21:42,574][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:21:42,575][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:21:42,582][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:21:42,584][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:21:42,584][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:21:42,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:43,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:44,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:45,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:46,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:46,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:47,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:48,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:49,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:50,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:50,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:51,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:52,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:53,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:53,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:54,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:55,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:56,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:57,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:57,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:21:59,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:00,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:00,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:01,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:02,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:03,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:03,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:04,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:05,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:06,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:07,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:07,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:09,546][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:22:10,549][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:22:10,551][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:22:11,988][__main__][INFO] - Iteration 267 took 55s (37.92% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 9m 47s. Estimated total time: 15h 21m 28s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 8s, 500 more iterations: 7h 40m 44s. +[2025-08-20 12:22:11,990][__main__][INFO] - Starting iteration 267. +[2025-08-20 12:22:35,290][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:22:35,427][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:22:35,438][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:22:37,881][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:22:37,882][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:22:37,888][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:22:37,891][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:22:37,891][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:22:38,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:38,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:39,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:40,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:41,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:42,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:42,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:43,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:44,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:45,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:46,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:46,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:47,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:48,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:49,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:50,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:50,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:51,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:52,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:53,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:54,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:54,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:56,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:56,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:57,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:58,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:22:59,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:00,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:00,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:01,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:02,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:03,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:04,782][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:23:05,886][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:23:05,889][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:23:07,336][__main__][INFO] - Iteration 268 took 55s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 9m 48s. Estimated total time: 15h 22m 25s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 12s. +[2025-08-20 12:23:07,337][__main__][INFO] - Starting iteration 268. +[2025-08-20 12:23:31,160][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:23:31,162][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:23:31,168][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:23:33,629][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:23:33,631][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:23:33,637][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:23:33,639][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:23:33,640][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:23:33,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:34,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:35,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:36,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:37,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:37,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:38,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:39,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:40,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:41,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:41,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:42,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:43,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:44,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:45,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:45,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:46,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:47,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:48,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:49,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:50,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:51,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:51,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:52,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:53,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:54,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:55,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:55,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:56,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:57,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:58,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:23:59,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:00,604][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:24:01,672][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:24:01,674][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:24:02,991][__main__][INFO] - Iteration 269 took 55s (38.38% Gen, 61.61% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 14m 0s. Estimated total time: 15h 27m 32s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 45s, 500 more iterations: 7h 43m 46s. +[2025-08-20 12:24:02,992][__main__][INFO] - Starting iteration 269. +[2025-08-20 12:24:26,449][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:24:26,451][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:24:26,457][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:24:28,902][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:24:28,903][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:24:28,910][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:24:28,912][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:24:28,913][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:24:29,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:30,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:30,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:31,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:32,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:33,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:33,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:34,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:35,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:36,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:37,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:37,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:38,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:39,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:40,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:41,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:41,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:42,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:43,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:44,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:45,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:46,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:47,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:47,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:48,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:49,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:50,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:51,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:51,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:52,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:53,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:54,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:24:55,843][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:24:56,875][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:24:56,877][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:24:58,196][__main__][INFO] - Iteration 270 took 55s (38.04% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 5m 35s. Estimated total time: 15h 20m 2s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 1s. +[2025-08-20 12:24:58,197][__main__][INFO] - Starting iteration 270. +[2025-08-20 12:25:21,447][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:25:21,448][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:25:21,455][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:25:23,893][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:25:23,894][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:25:23,901][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:25:23,903][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:25:23,903][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:25:24,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:24,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:25,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:26,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:27,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:28,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:28,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:29,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:30,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:31,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:32,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:32,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:33,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:34,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:35,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:36,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:36,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:37,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:38,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:39,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:40,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:40,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:41,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:42,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:43,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:44,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:45,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:46,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:46,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:47,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:48,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:49,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:25:50,843][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:25:51,784][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:25:51,786][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:25:53,201][__main__][INFO] - Iteration 271 took 55s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 1m 20s. Estimated total time: 15h 16m 42s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 21s. +[2025-08-20 12:25:53,202][__main__][INFO] - Starting iteration 271. +[2025-08-20 12:26:16,595][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:26:16,597][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:26:16,603][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:26:19,075][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:26:19,076][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:26:19,083][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:26:19,085][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:26:19,085][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:26:19,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:20,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:20,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:21,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:22,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:23,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:24,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:24,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:25,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:26,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:27,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:28,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:28,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:29,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:30,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:31,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:32,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:32,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:33,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:34,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:35,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:36,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:37,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:38,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:39,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:39,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:40,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:41,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:42,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:42,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:43,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:44,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:26:46,147][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:26:47,115][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:26:47,117][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:26:48,430][__main__][INFO] - Iteration 272 took 55s (37.89% Gen, 62.11% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 4m 10s. Estimated total time: 15h 20m 27s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 13s. +[2025-08-20 12:26:48,432][__main__][INFO] - Starting iteration 272. +[2025-08-20 12:27:11,713][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:27:11,715][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:27:11,721][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:27:14,177][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:27:14,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:27:14,184][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:27:14,186][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:27:14,187][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:27:14,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:15,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:16,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:16,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:17,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:18,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:19,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:20,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:20,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:21,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:22,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:23,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:24,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:24,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:25,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:26,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:27,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:27,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:28,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:29,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:30,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:31,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:32,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:33,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:33,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:34,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:35,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:36,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:37,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:37,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:38,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:39,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:27:41,136][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:27:42,254][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:27:42,256][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:27:43,695][__main__][INFO] - Iteration 273 took 55s (37.68% Gen, 62.32% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 3m 50s. Estimated total time: 15h 21m 2s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 31s. +[2025-08-20 12:27:43,698][__main__][INFO] - Starting iteration 273. +[2025-08-20 12:28:07,323][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:28:07,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:28:07,330][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:28:09,792][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:28:09,794][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:28:09,800][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:28:09,802][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:28:09,803][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:28:10,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:10,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:11,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:12,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:13,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:14,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:14,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:15,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:16,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:17,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:18,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:18,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:19,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:20,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:21,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:21,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:22,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:23,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:24,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:25,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:26,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:27,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:28,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:28,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:29,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:30,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:31,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:31,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:32,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:33,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:34,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:35,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:28:36,730][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:28:37,676][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:28:37,678][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:28:39,120][__main__][INFO] - Iteration 274 took 55s (38.20% Gen, 61.80% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 5m 34s. Estimated total time: 15h 23m 42s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 22s, 500 more iterations: 7h 41m 51s. +[2025-08-20 12:28:39,122][__main__][INFO] - Starting iteration 274. +[2025-08-20 12:29:02,363][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:02,364][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:02,370][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:04,832][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:04,833][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:04,840][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:04,842][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:29:04,842][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:29:05,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:05,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:06,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:07,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:08,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:09,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:09,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:10,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:11,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:12,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:13,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:13,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:14,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:15,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:16,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:17,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:17,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:18,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:19,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:20,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:21,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:21,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:22,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:23,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:24,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:25,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:26,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:27,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:28,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:29,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:29,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:30,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:29:32,193][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:29:33,125][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:29:33,126][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:29:34,401][__main__][INFO] - Iteration 275 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 2m 15s. Estimated total time: 15h 21m 18s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 7s, 500 more iterations: 7h 40m 39s. +[2025-08-20 12:29:34,402][__main__][INFO] - Starting iteration 275. +[2025-08-20 12:29:57,766][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:57,768][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:29:57,774][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:00,264][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:00,265][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:00,271][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:00,274][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:30:00,274][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:30:00,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:01,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:02,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:02,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:03,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:04,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:05,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:06,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:06,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:07,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:08,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:09,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:10,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:10,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:11,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:12,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:13,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:14,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:14,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:15,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:16,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:17,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:18,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:19,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:20,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:20,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:21,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:22,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:23,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:24,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:24,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:25,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:27,161][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:30:28,158][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:30:28,159][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:30:29,612][__main__][INFO] - Iteration 276 took 55s (37.78% Gen, 62.22% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 0m 10s. Estimated total time: 15h 20m 9s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 4s. +[2025-08-20 12:30:29,614][__main__][INFO] - Starting iteration 276. +[2025-08-20 12:30:52,980][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:52,992][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:53,004][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:55,462][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:55,463][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:55,470][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:30:55,472][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:30:55,472][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:30:55,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:56,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:57,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:58,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:59,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:30:59,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:00,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:01,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:02,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:02,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:03,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:04,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:05,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:06,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:06,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:07,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:08,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:09,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:10,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:10,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:11,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:12,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:13,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:14,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:14,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:15,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:16,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:17,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:18,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:19,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:20,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:20,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:22,508][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:31:23,475][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:31:23,476][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:31:24,934][__main__][INFO] - Iteration 277 took 55s (37.79% Gen, 62.21% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 1m 6s. Estimated total time: 15h 21m 59s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 59s. +[2025-08-20 12:31:24,936][__main__][INFO] - Starting iteration 277. +[2025-08-20 12:31:49,002][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:31:49,003][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:31:49,010][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:31:51,468][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:31:51,470][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:31:51,476][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:31:51,478][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:31:51,479][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:31:51,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:52,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:53,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:54,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:54,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:55,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:56,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:57,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:58,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:58,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:31:59,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:00,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:01,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:02,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:02,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:03,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:04,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:05,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:06,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:07,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:08,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:08,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:09,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:10,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:11,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:12,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:12,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:13,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:14,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:15,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:16,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:16,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:18,506][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:32:19,545][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:32:19,547][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:32:20,854][__main__][INFO] - Iteration 278 took 55s (38.60% Gen, 61.40% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 10m 1s. Estimated total time: 15h 31m 51s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 11s, 500 more iterations: 7h 45m 55s. +[2025-08-20 12:32:20,857][__main__][INFO] - Starting iteration 278. +[2025-08-20 12:32:44,280][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:32:44,281][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:32:44,287][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:32:46,777][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:32:46,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:32:46,784][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:32:46,786][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:32:46,787][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:32:47,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:47,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:48,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:49,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:50,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:51,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:51,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:52,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:53,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:54,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:55,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:55,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:56,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:57,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:58,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:58,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:32:59,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:00,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:01,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:02,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:03,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:04,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:04,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:05,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:06,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:07,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:08,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:08,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:09,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:10,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:11,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:12,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:13,702][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:33:14,659][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:33:14,661][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:33:16,041][__main__][INFO] - Iteration 279 took 55s (37.93% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 56m 58s. Estimated total time: 15h 19m 43s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 51s. +[2025-08-20 12:33:16,042][__main__][INFO] - Starting iteration 279. +[2025-08-20 12:33:39,366][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:33:39,367][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:33:39,374][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:33:41,825][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:33:41,827][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:33:41,834][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:33:41,836][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:33:41,836][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:33:42,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:42,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:43,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:44,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:45,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:46,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:46,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:47,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:48,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:49,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:50,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:50,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:51,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:52,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:53,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:54,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:54,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:55,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:56,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:57,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:58,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:33:58,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:00,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:00,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:01,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:02,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:03,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:04,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:04,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:05,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:06,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:07,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:08,798][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:34:09,772][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:34:09,774][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:34:11,224][__main__][INFO] - Iteration 280 took 55s (37.83% Gen, 62.17% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 56m 1s. Estimated total time: 15h 19m 41s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 50s. +[2025-08-20 12:34:11,226][__main__][INFO] - Starting iteration 280. +[2025-08-20 12:34:34,962][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:34:34,963][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:34:34,970][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:34:37,420][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:34:37,421][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:34:37,427][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:34:37,429][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:34:37,430][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:34:37,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:38,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:39,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:40,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:40,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:41,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:42,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:43,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:44,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:44,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:45,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:46,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:47,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:48,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:48,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:49,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:50,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:51,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:52,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:52,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:53,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:54,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:55,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:56,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:57,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:58,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:58,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:34:59,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:00,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:01,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:02,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:02,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:04,387][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:35:05,368][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:35:05,370][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:35:07,398][__main__][INFO] - Iteration 281 took 56s (37.88% Gen, 62.12% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 11m 36s. Estimated total time: 15h 36m 12s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 37s, 500 more iterations: 7h 48m 6s. +[2025-08-20 12:35:07,400][__main__][INFO] - Starting iteration 281. +[2025-08-20 12:35:31,717][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:35:31,718][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:35:31,724][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:35:34,175][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:35:34,177][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:35:34,183][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:35:34,185][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:35:34,185][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:35:34,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:35,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:36,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:36,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:37,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:38,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:39,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:40,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:40,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:41,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:42,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:43,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:43,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:44,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:45,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:46,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:47,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:47,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:48,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:49,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:50,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:51,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:52,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:53,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:53,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:54,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:55,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:56,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:57,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:57,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:58,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:35:59,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:01,097][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:36:02,082][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:36:02,084][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:36:03,535][__main__][INFO] - Iteration 282 took 56s (38.94% Gen, 61.06% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 10m 1s. Estimated total time: 15h 35m 34s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 33s, 500 more iterations: 7h 47m 47s. +[2025-08-20 12:36:03,536][__main__][INFO] - Starting iteration 282. +[2025-08-20 12:36:27,054][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:36:27,056][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:36:27,062][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:36:29,524][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:36:29,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:36:29,531][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:36:29,533][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:36:29,534][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:36:29,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:30,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:31,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:32,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:33,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:33,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:34,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:35,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:36,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:36,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:37,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:38,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:39,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:40,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:40,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:41,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:42,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:43,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:44,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:45,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:46,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:46,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:47,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:48,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:49,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:50,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:50,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:51,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:52,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:53,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:54,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:54,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:36:56,521][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:36:57,679][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:36:57,681][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:36:59,136][__main__][INFO] - Iteration 283 took 55s (37.90% Gen, 62.10% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 0m 10s. Estimated total time: 15h 26m 39s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 39s, 500 more iterations: 7h 43m 19s. +[2025-08-20 12:36:59,137][__main__][INFO] - Starting iteration 283. +[2025-08-20 12:37:22,441][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:37:22,443][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:37:22,449][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:37:24,927][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:37:24,929][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:37:24,935][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:37:24,937][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:37:24,938][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:37:25,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:26,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:26,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:27,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:28,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:29,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:29,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:30,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:31,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:32,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:33,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:33,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:34,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:35,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:36,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:37,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:37,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:38,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:39,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:40,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:41,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:42,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:43,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:44,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:44,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:45,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:46,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:47,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:48,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:48,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:49,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:50,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:37:52,001][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:37:52,966][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:37:52,968][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:37:54,282][__main__][INFO] - Iteration 284 took 55s (37.79% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 51m 41s. Estimated total time: 15h 19m 4s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 32s. +[2025-08-20 12:37:54,284][__main__][INFO] - Starting iteration 284. +[2025-08-20 12:38:17,502][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:38:17,503][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:38:17,510][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:38:19,960][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:38:19,962][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:38:19,968][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:38:19,970][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:38:19,971][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:38:20,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:21,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:21,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:22,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:23,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:24,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:25,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:25,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:26,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:27,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:28,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:29,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:29,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:30,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:31,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:32,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:32,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:33,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:34,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:35,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:36,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:37,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:38,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:38,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:39,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:40,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:41,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:42,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:42,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:43,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:44,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:45,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:38:46,907][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:38:47,849][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:38:47,850][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:38:49,219][__main__][INFO] - Iteration 285 took 54s (37.82% Gen, 62.18% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 47m 16s. Estimated total time: 15h 15m 35s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 47s. +[2025-08-20 12:38:49,221][__main__][INFO] - Starting iteration 285. +[2025-08-20 12:39:12,378][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:39:12,380][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:39:12,386][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:39:14,849][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:39:14,850][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:39:14,857][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:39:14,859][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:39:14,859][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:39:15,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:15,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:16,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:17,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:18,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:19,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:19,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:20,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:21,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:22,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:23,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:23,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:24,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:25,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:26,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:27,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:27,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:28,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:29,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:30,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:31,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:32,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:33,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:33,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:34,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:35,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:36,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:37,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:37,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:38,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:39,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:40,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:39:41,863][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:39:42,800][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:39:42,802][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:39:44,116][__main__][INFO] - Iteration 286 took 54s (37.70% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 45m 42s. Estimated total time: 15h 14m 55s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 27s. +[2025-08-20 12:39:44,118][__main__][INFO] - Starting iteration 286. +[2025-08-20 12:40:07,528][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:40:07,530][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:40:07,536][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:40:09,981][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:40:09,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:40:09,989][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:40:09,991][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:40:09,991][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:40:10,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:11,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:11,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:12,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:13,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:14,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:15,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:15,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:16,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:17,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:18,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:18,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:19,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:20,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:21,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:22,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:22,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:23,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:24,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:25,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:26,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:27,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:28,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:28,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:29,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:30,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:31,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:32,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:32,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:33,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:34,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:35,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:40:36,968][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:40:37,927][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:40:37,929][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:40:39,272][__main__][INFO] - Iteration 287 took 55s (38.02% Gen, 61.97% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 49m 5s. Estimated total time: 15h 19m 13s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 36s. +[2025-08-20 12:40:39,274][__main__][INFO] - Starting iteration 287. +[2025-08-20 12:41:02,652][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:02,654][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:02,660][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:05,114][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:05,116][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:05,122][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:05,124][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:41:05,125][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:41:05,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:06,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:07,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:07,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:08,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:09,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:10,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:10,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:11,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:12,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:13,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:14,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:14,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:15,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:16,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:17,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:18,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:18,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:19,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:20,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:21,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:22,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:22,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:23,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:24,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:25,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:26,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:27,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:28,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:28,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:29,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:30,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:41:32,069][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:41:33,505][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:41:33,507][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:41:34,933][__main__][INFO] - Iteration 288 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 56m 34s. Estimated total time: 15h 27m 38s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 45s, 500 more iterations: 7h 43m 49s. +[2025-08-20 12:41:34,934][__main__][INFO] - Starting iteration 288. +[2025-08-20 12:41:58,068][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:58,069][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:41:58,075][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:00,552][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:00,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:00,560][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:00,562][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:42:00,563][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:42:00,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:01,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:02,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:03,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:04,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:04,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:05,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:06,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:07,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:07,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:08,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:09,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:10,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:11,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:11,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:12,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:13,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:14,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:15,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:16,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:17,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:17,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:18,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:19,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:20,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:21,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:21,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:22,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:23,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:24,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:25,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:25,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:27,563][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:42:28,478][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:42:28,480][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:42:29,923][__main__][INFO] - Iteration 289 took 54s (37.61% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 44m 29s. Estimated total time: 15h 16m 28s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 14s. +[2025-08-20 12:42:29,924][__main__][INFO] - Starting iteration 289. +[2025-08-20 12:42:54,091][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:54,092][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:54,099][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:56,533][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:56,535][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:56,541][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:42:56,543][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:42:56,544][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:42:56,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:57,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:58,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:42:59,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:00,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:00,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:01,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:02,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:03,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:03,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:04,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:05,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:06,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:07,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:07,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:08,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:09,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:10,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:11,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:12,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:13,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:13,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:14,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:15,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:16,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:17,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:17,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:18,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:19,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:20,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:21,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:21,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:23,520][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:43:24,505][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:43:24,507][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:43:26,013][__main__][INFO] - Iteration 290 took 56s (38.72% Gen, 61.28% Train). Generation: 21s, Training: 34s. Estimated remaining time: 11h 1m 52s. Estimated total time: 15h 34m 47s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 28s, 500 more iterations: 7h 47m 23s. +[2025-08-20 12:43:26,014][__main__][INFO] - Starting iteration 290. +[2025-08-20 12:43:49,072][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:43:49,073][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:43:49,079][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:43:51,536][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:43:51,538][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:43:51,544][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:43:51,546][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:43:51,546][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:43:51,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:52,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:53,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:54,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:55,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:55,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:56,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:57,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:58,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:58,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:43:59,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:00,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:01,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:02,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:02,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:03,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:04,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:05,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:06,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:07,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:08,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:08,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:09,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:10,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:11,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:12,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:12,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:13,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:14,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:15,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:16,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:16,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:18,546][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:44:19,626][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:44:19,629][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:44:21,174][__main__][INFO] - Iteration 291 took 55s (37.36% Gen, 62.63% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 45m 29s. Estimated total time: 15h 19m 19s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 39s. +[2025-08-20 12:44:21,175][__main__][INFO] - Starting iteration 291. +[2025-08-20 12:44:44,298][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:44:44,299][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:44:44,306][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:44:46,765][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:44:46,767][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:44:46,773][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:44:46,775][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:44:46,776][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:44:47,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:47,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:48,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:49,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:50,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:51,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:51,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:52,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:53,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:54,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:55,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:55,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:56,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:57,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:58,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:58,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:44:59,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:00,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:01,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:02,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:02,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:03,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:05,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:05,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:06,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:07,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:08,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:09,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:09,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:10,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:11,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:12,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:13,810][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:45:14,889][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:45:14,891][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:45:16,212][__main__][INFO] - Iteration 292 took 55s (37.55% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 42m 31s. Estimated total time: 15h 17m 16s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 38s. +[2025-08-20 12:45:16,214][__main__][INFO] - Starting iteration 292. +[2025-08-20 12:45:39,673][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:45:39,674][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:45:39,681][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:45:42,124][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:45:42,125][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:45:42,132][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:45:42,134][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:45:42,135][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:45:42,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:43,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:44,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:44,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:45,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:46,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:47,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:47,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:48,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:49,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:50,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:51,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:51,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:52,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:53,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:54,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:55,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:55,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:56,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:57,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:58,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:45:59,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:00,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:01,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:01,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:02,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:03,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:04,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:05,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:05,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:06,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:07,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:09,132][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:46:10,032][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:46:10,034][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:46:11,366][__main__][INFO] - Iteration 293 took 55s (38.09% Gen, 61.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 43m 31s. Estimated total time: 15h 19m 11s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 35s. +[2025-08-20 12:46:11,368][__main__][INFO] - Starting iteration 293. +[2025-08-20 12:46:34,421][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:46:34,422][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:46:34,428][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:46:36,874][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:46:36,875][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:46:36,881][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:46:36,883][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:46:36,884][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:46:37,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:37,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:38,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:39,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:40,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:41,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:41,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:42,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:43,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:44,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:45,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:45,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:46,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:47,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:48,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:49,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:49,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:50,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:51,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:52,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:53,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:54,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:55,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:55,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:56,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:57,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:58,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:59,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:46:59,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:00,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:01,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:02,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:03,807][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:47:04,742][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:47:04,744][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:47:06,063][__main__][INFO] - Iteration 294 took 54s (37.70% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 35m 0s. Estimated total time: 15h 11m 35s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 9s, 500 more iterations: 7h 35m 47s. +[2025-08-20 12:47:06,065][__main__][INFO] - Starting iteration 294. +[2025-08-20 12:47:29,261][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:47:29,263][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:47:29,269][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:47:31,712][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:47:31,714][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:47:31,720][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:47:31,722][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:47:31,723][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:47:32,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:32,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:33,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:34,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:35,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:35,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:36,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:37,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:38,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:39,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:39,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:40,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:41,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:42,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:43,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:43,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:44,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:45,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:46,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:47,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:47,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:48,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:49,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:50,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:51,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:52,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:53,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:54,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:54,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:55,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:56,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:57,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:47:58,832][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:47:59,985][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:47:59,988][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:48:01,304][__main__][INFO] - Iteration 295 took 55s (37.59% Gen, 62.40% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 43m 8s. Estimated total time: 15h 20m 38s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 3s, 500 more iterations: 7h 40m 19s. +[2025-08-20 12:48:01,306][__main__][INFO] - Starting iteration 295. +[2025-08-20 12:48:24,378][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:48:24,379][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:48:24,386][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:48:26,829][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:48:26,830][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:48:26,837][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:48:26,839][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:48:26,839][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:48:27,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:27,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:28,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:29,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:30,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:31,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:31,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:32,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:33,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:34,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:35,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:35,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:36,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:37,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:38,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:39,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:39,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:40,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:41,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:42,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:43,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:44,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:45,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:45,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:46,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:47,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:48,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:49,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:49,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:50,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:51,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:52,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:48:53,823][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:48:54,771][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:48:54,773][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:48:56,157][__main__][INFO] - Iteration 296 took 54s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 35m 45s. Estimated total time: 15h 14m 10s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 25s, 500 more iterations: 7h 37m 5s. +[2025-08-20 12:48:56,158][__main__][INFO] - Starting iteration 296. +[2025-08-20 12:49:19,288][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:49:19,290][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:49:19,296][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:49:21,756][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:49:21,757][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:49:21,763][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:49:21,766][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:49:21,766][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:49:22,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:22,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:23,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:24,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:25,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:26,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:26,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:27,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:28,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:29,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:29,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:30,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:31,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:32,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:33,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:33,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:34,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:35,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:36,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:37,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:38,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:39,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:39,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:40,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:41,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:42,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:43,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:43,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:44,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:45,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:46,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:47,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:49:48,733][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:49:49,931][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:49:49,934][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:49:51,595][__main__][INFO] - Iteration 297 took 55s (37.29% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 44m 36s. Estimated total time: 15h 23m 57s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 23s, 500 more iterations: 7h 41m 58s. +[2025-08-20 12:49:51,597][__main__][INFO] - Starting iteration 297. +[2025-08-20 12:50:15,093][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:50:15,094][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:50:15,101][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:50:17,563][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:50:17,564][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:50:17,571][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:50:17,573][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:50:17,573][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:50:17,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:18,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:19,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:20,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:21,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:21,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:22,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:23,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:24,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:25,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:25,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:26,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:27,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:28,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:28,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:29,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:30,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:31,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:32,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:32,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:34,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:35,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:35,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:36,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:37,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:38,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:39,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:39,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:40,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:41,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:42,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:42,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:50:44,609][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:50:45,561][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:50:45,562][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:50:46,863][__main__][INFO] - Iteration 298 took 55s (38.03% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 40m 50s. Estimated total time: 15h 21m 5s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 32s. +[2025-08-20 12:50:46,865][__main__][INFO] - Starting iteration 298. +[2025-08-20 12:51:09,941][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:51:09,943][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:51:09,949][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:51:12,421][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:51:12,422][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:51:12,429][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:51:12,431][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:51:12,432][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:51:12,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:13,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:14,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:15,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:15,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:16,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:17,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:18,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:19,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:19,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:20,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:21,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:22,252][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:30,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:31,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:32,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:33,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:33,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:34,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:35,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:36,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:37,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:37,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:51:39,475][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:51:40,441][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:51:40,443][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:51:41,884][__main__][INFO] - Iteration 299 took 55s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 35m 47s. Estimated total time: 15h 16m 58s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 29s. +[2025-08-20 12:51:41,885][__main__][INFO] - Starting iteration 299. +[2025-08-20 12:52:05,039][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:52:05,040][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:52:05,047][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:52:07,502][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:52:07,504][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:52:07,511][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:52:07,513][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:52:07,513][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:52:07,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:08,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:09,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:10,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:10,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:11,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:12,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:13,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:14,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:14,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:15,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:16,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:17,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:18,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:18,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:19,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:20,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:21,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:22,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:22,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:23,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:24,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:25,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:26,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:26,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:27,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:28,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:29,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:30,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:31,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:32,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:32,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:52:34,496][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:52:35,594][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:52:35,596][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:52:37,050][__main__][INFO] - Iteration 300 took 55s (37.55% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 37m 18s. Estimated total time: 15h 19m 24s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 56s, 500 more iterations: 7h 39m 42s. +[2025-08-20 12:52:37,052][__main__][INFO] - Starting iteration 300. +[2025-08-20 12:53:00,102][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:00,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:00,110][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:02,569][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:02,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:02,577][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:02,579][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:53:02,580][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:53:02,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:03,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:04,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:05,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:06,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:06,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:07,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:08,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:09,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:10,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:10,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:11,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:12,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:13,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:13,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:14,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:15,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:16,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:17,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:17,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:19,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:20,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:20,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:21,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:22,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:23,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:24,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:24,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:25,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:26,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:27,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:27,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:53:29,620][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:53:30,581][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:53:30,583][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:53:34,419][__main__][INFO] - Iteration 301 took 57s (35.89% Gen, 59.70% Train). Generation: 20s, Training: 34s. Estimated remaining time: 11h 13m 3s. Estimated total time: 15h 56m 6s. Time estimates for 10 more iterations: 9m 33s, 100 more iterations: 1h 35m 36s, 500 more iterations: 7h 58m 3s. +[2025-08-20 12:53:34,420][__main__][INFO] - Starting iteration 301. +[2025-08-20 12:53:57,562][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:57,563][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:53:57,570][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:00,008][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:00,009][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:00,015][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:00,017][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:54:00,018][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:54:00,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:01,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:01,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:02,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:03,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:04,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:05,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:05,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:06,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:07,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:08,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:09,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:09,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:10,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:11,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:12,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:13,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:13,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:14,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:15,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:16,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:16,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:17,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:19,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:19,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:20,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:21,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:22,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:22,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:23,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:24,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:25,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:27,026][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:54:27,995][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:54:27,997][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:54:29,380][__main__][INFO] - Iteration 302 took 54s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 32m 1s. Estimated total time: 15h 15m 59s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 59s. +[2025-08-20 12:54:29,383][__main__][INFO] - Starting iteration 302. +[2025-08-20 12:54:52,943][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:52,944][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:52,951][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:55,417][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:55,418][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:55,425][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:54:55,427][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:54:55,428][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:54:55,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:56,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:57,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:58,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:58,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:54:59,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:00,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:01,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:02,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:02,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:03,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:04,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:05,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:06,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:06,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:07,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:08,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:09,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:10,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:10,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:11,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:12,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:13,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:14,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:15,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:16,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:16,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:17,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:18,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:19,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:19,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:20,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:22,415][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:55:23,491][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:55:23,494][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:55:24,929][__main__][INFO] - Iteration 303 took 55s (37.98% Gen, 62.02% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 40m 47s. Estimated total time: 15h 25m 41s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 34s, 500 more iterations: 7h 42m 50s. +[2025-08-20 12:55:24,932][__main__][INFO] - Starting iteration 303. +[2025-08-20 12:55:48,059][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:55:48,060][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:55:48,067][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:55:50,535][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:55:50,536][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:55:50,542][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:55:50,545][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:55:50,545][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:55:50,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:51,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:52,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:53,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:54,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:54,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:55,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:56,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:57,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:57,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:58,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:55:59,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:00,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:01,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:01,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:02,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:03,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:04,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:05,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:05,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:07,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:08,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:08,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:09,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:10,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:11,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:11,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:12,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:13,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:14,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:15,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:15,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:17,571][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:56:18,525][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:56:18,527][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:56:19,952][__main__][INFO] - Iteration 304 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 31m 11s. Estimated total time: 15h 17m 0s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 30s. +[2025-08-20 12:56:19,954][__main__][INFO] - Starting iteration 304. +[2025-08-20 12:56:43,125][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:56:43,126][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:56:43,132][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:56:45,573][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:56:45,575][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:56:45,581][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:56:45,583][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:56:45,584][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:56:45,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:46,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:47,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:48,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:49,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:49,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:50,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:51,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:52,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:53,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:53,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:54,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:55,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:56,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:56,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:57,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:58,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:56:59,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:00,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:01,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:02,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:02,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:03,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:04,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:05,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:06,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:06,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:07,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:08,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:09,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:10,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:10,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:12,542][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:57:13,649][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:57:13,651][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:57:15,214][__main__][INFO] - Iteration 305 took 55s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 34m 15s. Estimated total time: 15h 20m 59s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 5s, 500 more iterations: 7h 40m 29s. +[2025-08-20 12:57:15,216][__main__][INFO] - Starting iteration 305. +[2025-08-20 12:57:38,746][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:57:38,748][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:57:38,754][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:57:41,206][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:57:41,208][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:57:41,214][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:57:41,217][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:57:41,217][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:57:41,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:42,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:43,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:43,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:44,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:45,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:46,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:47,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:47,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:48,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:49,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:50,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:51,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:51,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:52,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:53,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:54,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:55,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:55,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:56,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:57,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:58,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:57:59,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:00,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:01,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:01,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:02,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:03,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:04,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:04,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:05,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:06,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:08,134][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:58:09,091][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:58:09,093][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:58:10,446][__main__][INFO] - Iteration 306 took 55s (38.16% Gen, 61.84% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 32m 50s. Estimated total time: 15h 20m 29s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 14s. +[2025-08-20 12:58:10,447][__main__][INFO] - Starting iteration 306. +[2025-08-20 12:58:34,568][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:58:34,569][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:58:34,575][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:58:37,035][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:58:37,036][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:58:37,042][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:58:37,045][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:58:37,045][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:58:37,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:38,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:38,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:39,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:40,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:41,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:42,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:42,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:43,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:44,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:45,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:46,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:46,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:47,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:48,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:49,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:50,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:50,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:51,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:52,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:53,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:53,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:54,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:56,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:56,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:57,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:58,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:58:59,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:00,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:00,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:01,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:02,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:04,079][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 12:59:05,067][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 12:59:05,068][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 12:59:06,669][__main__][INFO] - Iteration 307 took 56s (38.57% Gen, 61.42% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 48m 25s. Estimated total time: 15h 37m 1s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 42s, 500 more iterations: 7h 48m 30s. +[2025-08-20 12:59:06,671][__main__][INFO] - Starting iteration 307. +[2025-08-20 12:59:30,577][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:59:30,578][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:59:30,585][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:59:33,064][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:59:33,065][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:59:33,071][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 12:59:33,074][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 12:59:33,074][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 12:59:33,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:34,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:34,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:35,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:36,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:37,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:38,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:38,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:39,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:40,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:41,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:42,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:42,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:43,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:44,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:45,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:46,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:47,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:48,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:48,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:49,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:50,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:51,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:52,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:52,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:53,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:54,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:55,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:56,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:56,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:57,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:58,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 12:59:59,972][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:00:00,937][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:00:00,938][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:00:02,865][__main__][INFO] - Iteration 308 took 56s (38.14% Gen, 61.86% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 47m 1s. Estimated total time: 15h 36m 33s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 39s, 500 more iterations: 7h 48m 16s. +[2025-08-20 13:00:02,866][__main__][INFO] - Starting iteration 308. +[2025-08-20 13:00:25,999][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:00:26,001][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:00:26,007][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:00:28,475][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:00:28,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:00:28,483][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:00:28,485][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:00:28,486][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:00:28,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:29,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:30,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:31,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:31,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:32,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:33,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:34,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:35,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:35,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:36,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:37,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:38,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:39,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:39,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:40,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:41,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:42,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:43,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:43,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:44,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:45,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:46,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:47,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:48,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:49,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:49,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:50,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:51,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:52,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:53,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:53,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:00:55,402][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:00:56,394][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:00:56,396][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:00:57,729][__main__][INFO] - Iteration 309 took 54s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 23m 55s. Estimated total time: 15h 14m 22s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 11s. +[2025-08-20 13:00:57,730][__main__][INFO] - Starting iteration 309. +[2025-08-20 13:01:20,828][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:01:20,829][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:01:20,836][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:01:23,291][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:01:23,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:01:23,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:01:23,301][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:01:23,301][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:01:23,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:24,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:25,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:25,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:26,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:27,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:28,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:29,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:29,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:30,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:31,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:32,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:33,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:33,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:34,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:35,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:36,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:37,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:37,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:38,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:39,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:40,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:41,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:42,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:43,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:43,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:44,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:45,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:46,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:47,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:47,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:48,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:01:50,279][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:01:51,286][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:01:51,288][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:01:52,632][__main__][INFO] - Iteration 310 took 54s (37.61% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 23m 39s. Estimated total time: 15h 15m 1s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 30s. +[2025-08-20 13:01:52,633][__main__][INFO] - Starting iteration 310. +[2025-08-20 13:02:16,861][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:02:16,863][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:02:16,869][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:02:19,327][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:02:19,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:02:19,335][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:02:19,337][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:02:19,338][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:02:19,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:20,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:21,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:22,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:22,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:23,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:24,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:25,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:25,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:26,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:27,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:28,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:29,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:29,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:30,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:31,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:32,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:33,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:34,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:35,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:35,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:36,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:37,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:38,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:39,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:39,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:40,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:41,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:42,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:43,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:43,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:44,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:02:46,251][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:02:47,541][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:02:47,544][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:02:48,993][__main__][INFO] - Iteration 311 took 56s (38.63% Gen, 61.37% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 47m 0s. Estimated total time: 15h 39m 18s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 55s, 500 more iterations: 7h 49m 39s. +[2025-08-20 13:02:48,994][__main__][INFO] - Starting iteration 311. +[2025-08-20 13:03:12,571][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:03:12,573][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:03:12,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:03:15,021][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:03:15,022][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:03:15,029][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:03:15,031][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:03:15,031][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:03:15,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:16,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:16,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:17,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:18,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:19,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:20,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:20,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:21,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:22,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:23,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:24,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:24,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:25,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:26,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:27,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:28,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:29,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:30,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:30,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:31,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:32,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:33,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:34,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:34,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:35,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:36,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:37,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:38,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:38,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:39,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:40,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:03:42,007][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:03:43,103][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:03:43,106][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:03:44,447][__main__][INFO] - Iteration 312 took 55s (38.11% Gen, 61.88% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 30m 59s. Estimated total time: 15h 24m 12s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 25s, 500 more iterations: 7h 42m 6s. +[2025-08-20 13:03:44,449][__main__][INFO] - Starting iteration 312. +[2025-08-20 13:04:07,892][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:04:07,893][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:04:07,899][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:04:10,334][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:04:10,336][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:04:10,342][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:04:10,344][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:04:10,345][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:04:10,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:11,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:12,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:13,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:13,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:14,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:15,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:16,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:16,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:17,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:18,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:19,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:20,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:20,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:21,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:22,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:23,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:24,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:24,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:26,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:26,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:27,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:28,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:29,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:30,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:30,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:31,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:32,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:33,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:34,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:34,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:35,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:04:37,268][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:04:38,301][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:04:38,303][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:04:39,832][__main__][INFO] - Iteration 313 took 55s (37.94% Gen, 62.06% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 28m 54s. Estimated total time: 15h 23m 3s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 18s, 500 more iterations: 7h 41m 31s. +[2025-08-20 13:04:39,834][__main__][INFO] - Starting iteration 313. +[2025-08-20 13:05:02,980][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:02,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:02,988][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:05,405][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:05,406][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:05,413][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:05,415][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:05:05,415][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:05:05,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:06,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:07,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:08,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:08,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:09,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:10,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:11,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:12,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:12,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:13,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:14,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:15,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:16,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:16,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:17,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:18,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:19,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:19,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:20,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:22,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:22,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:23,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:24,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:25,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:25,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:26,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:27,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:28,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:29,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:29,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:30,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:05:32,307][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:05:33,244][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:05:33,247][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:05:35,351][__main__][INFO] - Iteration 314 took 55s (37.28% Gen, 62.72% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 30m 12s. Estimated total time: 15h 25m 16s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 31s, 500 more iterations: 7h 42m 38s. +[2025-08-20 13:05:35,352][__main__][INFO] - Starting iteration 314. +[2025-08-20 13:05:59,042][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:59,044][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:05:59,050][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:01,515][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:01,516][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:01,523][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:01,525][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:06:01,526][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:06:01,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:02,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:03,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:04,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:04,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:05,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:06,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:07,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:08,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:08,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:09,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:10,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:11,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:12,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:12,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:13,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:14,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:15,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:16,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:16,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:18,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:18,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:19,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:20,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:21,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:22,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:22,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:23,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:24,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:25,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:26,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:26,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:28,485][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:06:29,604][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:06:29,606][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:06:31,124][__main__][INFO] - Iteration 315 took 55s (38.07% Gen, 61.93% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 33m 30s. Estimated total time: 15h 29m 30s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 57s, 500 more iterations: 7h 44m 45s. +[2025-08-20 13:06:31,125][__main__][INFO] - Starting iteration 315. +[2025-08-20 13:06:55,298][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:55,300][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:55,306][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:57,765][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:57,767][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:57,773][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:06:57,775][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:06:57,776][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:06:58,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:58,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:06:59,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:00,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:01,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:02,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:02,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:03,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:04,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:05,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:05,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:06,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:07,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:08,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:09,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:09,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:10,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:11,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:12,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:13,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:13,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:15,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:15,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:16,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:17,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:18,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:19,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:19,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:20,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:21,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:22,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:23,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:24,676][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:07:25,574][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:07:25,575][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:07:27,100][__main__][INFO] - Iteration 316 took 55s (38.81% Gen, 61.19% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 35m 58s. Estimated total time: 15h 32m 54s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 17s, 500 more iterations: 7h 46m 27s. +[2025-08-20 13:07:27,102][__main__][INFO] - Starting iteration 316. +[2025-08-20 13:07:50,940][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:07:50,941][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:07:50,947][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:07:53,402][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:07:53,403][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:07:53,409][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:07:53,412][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:07:53,412][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:07:53,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:54,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:55,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:56,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:56,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:57,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:58,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:07:59,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:00,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:00,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:01,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:02,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:03,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:04,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:04,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:05,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:06,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:07,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:07,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:08,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:09,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:10,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:11,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:12,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:13,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:13,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:14,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:15,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:16,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:17,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:17,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:18,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:20,294][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:08:21,234][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:08:21,235][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:08:23,632][__main__][INFO] - Iteration 317 took 56s (37.82% Gen, 62.18% Train). Generation: 21s, Training: 35s. Estimated remaining time: 10h 44m 17s. Estimated total time: 15h 42m 10s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 13s, 500 more iterations: 7h 51m 5s. +[2025-08-20 13:08:23,634][__main__][INFO] - Starting iteration 317. +[2025-08-20 13:08:46,814][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:08:46,815][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:08:46,822][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:08:49,313][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:08:49,315][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:08:49,321][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:08:49,323][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:08:49,324][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:08:49,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:50,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:51,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:52,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:52,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:53,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:54,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:55,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:55,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:56,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:57,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:58,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:59,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:08:59,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:00,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:01,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:02,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:03,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:04,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:05,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:05,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:06,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:07,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:08,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:09,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:09,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:10,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:11,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:12,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:13,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:13,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:14,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:16,339][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:09:17,324][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:09:17,326][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:09:19,133][__main__][INFO] - Iteration 318 took 55s (37.27% Gen, 62.73% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 26m 10s. Estimated total time: 15h 24m 58s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 29s, 500 more iterations: 7h 42m 29s. +[2025-08-20 13:09:19,135][__main__][INFO] - Starting iteration 318. +[2025-08-20 13:09:44,307][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:09:44,309][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:09:44,315][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:09:46,764][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:09:46,766][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:09:46,772][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:09:46,775][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:09:46,775][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:09:47,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:47,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:48,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:49,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:50,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:51,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:51,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:52,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:53,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:54,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:55,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:55,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:56,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:57,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:58,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:58,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:09:59,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:00,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:01,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:02,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:02,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:03,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:04,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:05,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:06,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:07,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:08,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:08,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:09,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:10,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:11,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:12,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:13,775][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:10:14,730][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:10:14,732][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:10:16,307][__main__][INFO] - Iteration 319 took 57s (39.77% Gen, 60.23% Train). Generation: 22s, Training: 34s. Estimated remaining time: 10h 53m 6s. Estimated total time: 15h 52m 51s. Time estimates for 10 more iterations: 9m 31s, 100 more iterations: 1h 35m 17s, 500 more iterations: 7h 56m 25s. +[2025-08-20 13:10:16,308][__main__][INFO] - Starting iteration 319. +[2025-08-20 13:10:39,379][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:10:39,380][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:10:39,387][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:10:41,851][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:10:41,852][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:10:41,859][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:10:41,861][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:10:41,862][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:10:42,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:42,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:43,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:44,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:45,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:46,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:46,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:47,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:48,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:49,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:50,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:50,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:51,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:52,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:53,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:54,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:54,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:55,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:56,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:57,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:58,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:10:59,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:00,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:00,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:01,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:02,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:03,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:04,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:04,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:05,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:06,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:07,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:08,763][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:11:09,739][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:11:09,741][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:11:11,263][__main__][INFO] - Iteration 320 took 54s (37.51% Gen, 62.49% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 15m 14s. Estimated total time: 15h 15m 54s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 57s. +[2025-08-20 13:11:11,264][__main__][INFO] - Starting iteration 320. +[2025-08-20 13:11:34,458][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:11:34,459][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:11:34,465][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:11:36,939][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:11:36,940][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:11:36,946][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:11:36,949][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:11:36,949][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:11:37,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:38,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:38,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:39,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:40,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:41,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:42,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:42,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:43,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:44,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:45,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:45,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:46,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:47,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:48,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:49,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:49,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:50,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:51,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:52,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:53,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:53,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:54,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:55,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:56,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:57,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:58,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:11:59,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:00,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:00,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:01,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:02,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:03,969][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:12:04,957][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:12:04,959][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:12:06,340][__main__][INFO] - Iteration 321 took 55s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 16m 19s. Estimated total time: 15h 17m 55s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 57s. +[2025-08-20 13:12:06,342][__main__][INFO] - Starting iteration 321. +[2025-08-20 13:12:29,451][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:12:29,453][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:12:29,459][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:12:31,924][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:12:31,925][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:12:31,931][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:12:31,934][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:12:31,934][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:12:32,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:33,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:33,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:34,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:35,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:36,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:36,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:37,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:38,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:39,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:40,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:40,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:41,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:42,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:43,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:44,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:44,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:46,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:46,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:47,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:48,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:49,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:50,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:50,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:51,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:52,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:53,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:54,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:54,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:55,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:56,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:57,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:12:58,898][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:13:00,055][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:13:00,058][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:13:01,570][__main__][INFO] - Iteration 322 took 55s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 17m 57s. Estimated total time: 15h 20m 28s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 14s. +[2025-08-20 13:13:01,572][__main__][INFO] - Starting iteration 322. +[2025-08-20 13:13:24,966][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:13:24,968][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:13:24,974][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:13:27,427][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:13:27,429][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:13:27,435][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:13:27,437][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:13:27,438][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:13:27,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:28,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:29,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:30,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:30,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:31,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:32,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:33,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:34,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:34,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:35,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:36,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:37,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:38,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:38,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:39,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:40,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:41,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:42,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:43,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:44,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:44,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:45,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:46,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:47,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:48,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:48,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:49,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:50,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:51,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:52,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:52,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:13:54,391][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:13:55,331][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:13:55,333][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:13:56,687][__main__][INFO] - Iteration 323 took 55s (37.97% Gen, 62.03% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 15m 9s. Estimated total time: 15h 18m 35s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 51s, 500 more iterations: 7h 39m 17s. +[2025-08-20 13:13:56,689][__main__][INFO] - Starting iteration 323. +[2025-08-20 13:14:19,895][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:14:19,896][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:14:19,903][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:14:22,376][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:14:22,378][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:14:22,384][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:14:22,386][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:14:22,387][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:14:22,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:23,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:24,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:25,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:25,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:26,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:27,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:28,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:29,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:29,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:30,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:31,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:32,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:33,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:33,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:34,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:35,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:36,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:36,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:37,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:38,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:39,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:40,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:41,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:42,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:43,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:43,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:44,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:45,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:46,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:47,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:47,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:14:49,414][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:14:50,464][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:14:50,466][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:14:51,851][__main__][INFO] - Iteration 324 took 55s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 15m 0s. Estimated total time: 15h 19m 21s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 56s, 500 more iterations: 7h 39m 40s. +[2025-08-20 13:14:51,852][__main__][INFO] - Starting iteration 324. +[2025-08-20 13:15:14,897][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:15:14,898][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:15:14,905][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:15:17,359][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:15:17,361][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:15:17,367][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:15:17,369][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:15:17,370][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:15:17,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:18,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:19,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:20,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:20,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:21,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:22,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:23,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:24,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:24,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:25,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:26,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:27,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:27,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:28,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:29,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:30,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:31,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:31,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:32,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:33,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:34,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:35,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:36,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:37,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:37,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:38,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:39,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:40,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:41,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:41,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:42,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:15:44,344][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:15:45,293][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:15:45,294][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:15:46,789][__main__][INFO] - Iteration 325 took 54s (37.46% Gen, 62.53% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 10m 20s. Estimated total time: 15h 15m 36s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 48s. +[2025-08-20 13:15:46,791][__main__][INFO] - Starting iteration 325. +[2025-08-20 13:16:10,419][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:16:10,421][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:16:10,427][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:16:12,867][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:16:12,868][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:16:12,875][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:16:12,877][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:16:12,877][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:16:13,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:13,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:14,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:15,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:16,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:17,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:17,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:18,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:19,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:20,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:21,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:21,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:22,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:23,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:24,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:25,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:25,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:26,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:27,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:28,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:29,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:30,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:31,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:31,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:32,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:33,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:34,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:35,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:35,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:36,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:37,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:38,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:16:39,797][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:16:40,747][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:16:40,748][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:16:42,147][__main__][INFO] - Iteration 326 took 55s (38.27% Gen, 61.72% Train). Generation: 21s, Training: 34s. Estimated remaining time: 10h 16m 24s. Estimated total time: 15h 22m 35s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 17s. +[2025-08-20 13:16:42,148][__main__][INFO] - Starting iteration 326. +[2025-08-20 13:17:05,260][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:17:05,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:17:05,268][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:17:07,740][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:17:07,742][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:17:07,748][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:17:07,750][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:17:07,751][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:17:08,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:08,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:09,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:10,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:11,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:12,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:12,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:13,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:14,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:15,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:15,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:16,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:17,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:18,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:19,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:19,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:20,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:21,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:22,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:23,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:23,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:25,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:25,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:26,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:27,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:28,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:29,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:29,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:30,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:31,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:32,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:33,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:17:34,730][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:17:35,694][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:17:35,696][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:17:37,332][__main__][INFO] - Iteration 327 took 55s (37.41% Gen, 62.59% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 12m 37s. Estimated total time: 15h 19m 43s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 51s. +[2025-08-20 13:17:37,334][__main__][INFO] - Starting iteration 327. +[2025-08-20 13:18:02,272][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:02,275][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:02,282][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:04,723][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:04,724][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:04,730][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:04,733][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:18:04,733][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:18:05,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:05,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:06,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:07,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:08,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:09,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:09,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:10,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:11,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:12,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:12,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:13,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:14,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:15,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:16,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:16,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:17,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:18,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:19,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:20,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:21,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:22,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:23,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:23,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:24,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:25,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:26,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:27,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:27,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:28,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:29,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:30,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:18:31,832][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:18:32,865][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:18:32,868][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:18:34,209][__main__][INFO] - Iteration 328 took 56s (39.55% Gen, 60.45% Train). Generation: 22s, Training: 34s. Estimated remaining time: 10h 39m 51s. Estimated total time: 15h 47m 54s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 47s, 500 more iterations: 7h 53m 57s. +[2025-08-20 13:18:34,213][__main__][INFO] - Starting iteration 328. +[2025-08-20 13:18:57,346][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:57,347][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:57,353][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:59,795][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:59,796][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:59,803][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:18:59,805][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:18:59,805][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:19:00,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:00,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:01,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:02,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:03,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:04,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:04,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:05,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:06,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:07,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:08,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:08,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:09,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:10,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:11,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:12,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:12,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:13,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:14,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:15,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:15,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:16,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:17,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:18,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:19,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:20,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:21,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:21,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:22,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:23,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:24,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:25,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:26,721][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:19:27,717][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:19:27,718][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:19:29,101][__main__][INFO] - Iteration 329 took 54s (37.68% Gen, 62.32% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 5m 49s. Estimated total time: 15h 14m 47s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 23s. +[2025-08-20 13:19:29,102][__main__][INFO] - Starting iteration 329. +[2025-08-20 13:19:52,214][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:19:52,216][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:19:52,222][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:19:54,667][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:19:54,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:19:54,675][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:19:54,677][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:19:54,677][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:19:54,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:55,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:56,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:57,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:58,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:58,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:19:59,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:00,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:01,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:02,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:02,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:03,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:04,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:05,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:06,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:06,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:07,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:08,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:09,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:10,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:10,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:11,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:12,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:13,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:14,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:14,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:16,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:16,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:17,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:18,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:19,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:20,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:21,651][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:20:22,614][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:20:22,615][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:20:23,982][__main__][INFO] - Iteration 330 took 54s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 4m 46s. Estimated total time: 15h 14m 39s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 19s. +[2025-08-20 13:20:23,984][__main__][INFO] - Starting iteration 330. +[2025-08-20 13:20:47,192][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:20:47,193][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:20:47,199][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:20:49,652][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:20:49,653][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:20:49,660][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:20:49,662][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:20:49,662][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:20:49,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:50,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:51,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:52,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:53,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:53,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:54,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:55,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:56,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:57,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:57,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:58,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:20:59,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:00,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:01,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:01,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:02,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:03,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:04,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:05,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:05,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:06,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:07,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:08,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:09,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:10,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:11,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:11,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:12,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:13,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:14,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:15,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:16,598][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:21:17,586][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:21:17,588][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:21:18,980][__main__][INFO] - Iteration 331 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 5m 47s. Estimated total time: 15h 16m 35s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 17s. +[2025-08-20 13:21:18,982][__main__][INFO] - Starting iteration 331. +[2025-08-20 13:21:42,051][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:21:42,053][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:21:42,059][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:21:44,522][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:21:44,523][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:21:44,530][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:21:44,532][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:21:44,533][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:21:44,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:45,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:46,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:47,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:48,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:48,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:49,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:50,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:51,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:51,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:52,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:53,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:54,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:55,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:55,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:56,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:57,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:58,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:21:59,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:00,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:01,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:01,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:02,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:03,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:04,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:05,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:05,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:06,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:07,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:08,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:09,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:09,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:11,421][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:22:12,385][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:22:12,386][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:22:13,797][__main__][INFO] - Iteration 332 took 54s (37.60% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 1m 52s. Estimated total time: 15h 13m 35s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 47s. +[2025-08-20 13:22:13,799][__main__][INFO] - Starting iteration 332. +[2025-08-20 13:22:37,127][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:22:37,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:22:37,135][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:22:39,605][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:22:39,607][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:22:39,613][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:22:39,615][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:22:39,616][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:22:39,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:40,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:41,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:42,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:43,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:43,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:44,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:45,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:46,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:47,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:47,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:48,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:49,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:50,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:51,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:51,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:52,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:53,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:54,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:54,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:55,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:56,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:57,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:58,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:22:59,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:00,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:01,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:01,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:02,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:03,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:04,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:05,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:06,607][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:23:07,531][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:23:07,532][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:23:08,894][__main__][INFO] - Iteration 333 took 55s (37.87% Gen, 62.13% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 5m 36s. Estimated total time: 15h 18m 14s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 7s. +[2025-08-20 13:23:08,895][__main__][INFO] - Starting iteration 333. +[2025-08-20 13:23:32,055][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:23:32,056][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:23:32,062][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:23:34,505][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:23:34,506][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:23:34,513][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:23:34,515][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:23:34,516][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:23:34,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:35,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:36,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:37,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:37,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:38,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:39,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:40,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:41,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:41,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:42,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:43,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:44,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:45,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:45,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:46,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:47,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:48,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:49,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:49,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:50,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:51,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:52,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:53,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:54,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:55,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:55,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:56,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:57,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:58,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:59,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:23:59,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:01,520][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:24:02,790][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:24:02,793][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:24:04,198][__main__][INFO] - Iteration 334 took 55s (37.46% Gen, 62.54% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 8m 8s. Estimated total time: 15h 21m 42s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 10s, 500 more iterations: 7h 40m 51s. +[2025-08-20 13:24:04,199][__main__][INFO] - Starting iteration 334. +[2025-08-20 13:24:27,381][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:24:27,382][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:24:27,388][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:24:29,844][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:24:29,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:24:29,851][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:24:29,853][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:24:29,854][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:24:30,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:30,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:31,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:32,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:33,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:34,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:34,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:35,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:36,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:37,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:38,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:38,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:39,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:40,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:41,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:42,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:42,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:43,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:44,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:45,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:46,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:47,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:48,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:48,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:49,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:50,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:51,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:52,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:52,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:53,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:54,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:55,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:24:56,759][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:24:57,718][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:24:57,720][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:24:59,045][__main__][INFO] - Iteration 335 took 54s (37.80% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 59m 37s. Estimated total time: 15h 14m 5s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 2s. +[2025-08-20 13:24:59,047][__main__][INFO] - Starting iteration 335. +[2025-08-20 13:25:22,241][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:25:22,243][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:25:22,249][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:25:24,723][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:25:24,724][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:25:24,731][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:25:24,734][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:25:24,734][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:25:25,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:25,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:26,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:27,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:28,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:29,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:29,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:30,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:31,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:32,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:32,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:33,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:34,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:35,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:36,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:36,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:37,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:38,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:39,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:40,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:41,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:42,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:42,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:43,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:44,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:45,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:46,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:46,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:47,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:48,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:49,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:50,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:25:51,745][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:25:52,761][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:25:52,763][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:25:54,163][__main__][INFO] - Iteration 336 took 55s (37.58% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 3m 12s. Estimated total time: 15h 18m 35s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 51s, 500 more iterations: 7h 39m 17s. +[2025-08-20 13:25:54,164][__main__][INFO] - Starting iteration 336. +[2025-08-20 13:26:17,380][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:26:18,590][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:26:18,600][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:26:21,085][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:26:21,087][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:26:21,093][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:26:21,096][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:26:21,096][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:26:21,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:22,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:22,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:23,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:24,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:25,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:26,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:26,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:27,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:28,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:29,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:30,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:30,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:31,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:32,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:33,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:34,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:34,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:35,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:36,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:37,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:38,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:39,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:40,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:40,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:41,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:42,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:43,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:44,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:44,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:45,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:46,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:26:48,104][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:26:49,103][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:26:49,104][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:26:50,489][__main__][INFO] - Iteration 337 took 56s (36.79% Gen, 63.21% Train). Generation: 20s, Training: 35s. Estimated remaining time: 10h 22m 24s. Estimated total time: 15h 38m 44s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 52s, 500 more iterations: 7h 49m 22s. +[2025-08-20 13:26:50,490][__main__][INFO] - Starting iteration 337. +[2025-08-20 13:27:13,879][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:27:13,881][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:27:13,887][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:27:16,346][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:27:16,347][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:27:16,354][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:27:16,356][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:27:16,357][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:27:16,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:17,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:18,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:19,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:19,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:20,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:21,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:22,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:23,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:23,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:24,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:25,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:26,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:26,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:27,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:28,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:29,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:30,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:30,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:31,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:32,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:33,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:34,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:35,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:36,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:37,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:37,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:38,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:39,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:40,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:40,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:41,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:27:43,348][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:27:44,546][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:27:44,548][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:27:45,987][__main__][INFO] - Iteration 338 took 55s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 10h 7m 41s. Estimated total time: 15h 24m 56s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 29s, 500 more iterations: 7h 42m 28s. +[2025-08-20 13:27:45,989][__main__][INFO] - Starting iteration 338. +[2025-08-20 13:28:09,174][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:28:09,176][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:28:09,182][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:28:11,645][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:28:11,646][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:28:11,653][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:28:11,656][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:28:11,656][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:28:11,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:12,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:13,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:14,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:15,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:15,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:16,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:17,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:18,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:19,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:19,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:20,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:21,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:22,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:23,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:23,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:24,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:25,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:26,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:27,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:27,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:28,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:29,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:30,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:31,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:32,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:33,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:33,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:34,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:35,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:36,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:37,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:28:38,704][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:28:39,653][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:28:39,654][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:28:40,952][__main__][INFO] - Iteration 339 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 57m 52s. Estimated total time: 15h 16m 2s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 1s. +[2025-08-20 13:28:40,953][__main__][INFO] - Starting iteration 339. +[2025-08-20 13:29:03,982][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:03,983][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:03,990][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:06,450][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:06,451][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:06,458][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:06,461][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:29:06,461][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:29:06,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:07,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:08,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:09,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:09,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:10,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:11,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:12,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:13,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:13,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:14,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:15,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:16,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:17,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:17,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:18,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:19,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:20,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:21,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:21,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:22,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:23,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:24,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:24,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:26,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:27,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:27,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:28,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:29,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:30,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:31,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:31,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:29:33,452][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:29:34,521][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:29:34,523][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:29:35,856][__main__][INFO] - Iteration 340 took 54s (37.50% Gen, 62.49% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 55m 58s. Estimated total time: 15h 15m 3s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 31s. +[2025-08-20 13:29:35,858][__main__][INFO] - Starting iteration 340. +[2025-08-20 13:29:58,852][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:58,853][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:29:58,859][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:01,317][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:01,319][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:01,325][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:01,328][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:30:01,328][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:30:01,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:02,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:03,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:04,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:04,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:05,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:06,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:07,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:07,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:08,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:09,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:10,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:11,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:11,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:12,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:13,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:14,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:15,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:15,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:16,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:17,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:18,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:19,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:20,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:21,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:21,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:22,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:23,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:24,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:25,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:25,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:26,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:28,254][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:30:29,231][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:30:29,233][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:30:30,647][__main__][INFO] - Iteration 341 took 54s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 53m 9s. Estimated total time: 15h 13m 8s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 18s, 500 more iterations: 7h 36m 34s. +[2025-08-20 13:30:30,648][__main__][INFO] - Starting iteration 341. +[2025-08-20 13:30:53,800][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:53,802][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:53,809][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:56,267][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:56,268][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:56,274][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:30:56,277][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:30:56,277][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:30:56,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:57,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:58,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:58,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:30:59,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:00,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:01,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:02,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:02,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:03,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:04,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:05,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:06,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:06,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:07,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:08,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:09,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:10,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:10,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:11,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:12,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:13,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:14,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:15,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:16,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:16,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:17,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:18,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:19,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:20,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:20,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:21,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:23,311][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:31:24,279][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:31:24,281][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:31:25,581][__main__][INFO] - Iteration 342 took 54s (37.70% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 54m 38s. Estimated total time: 15h 15m 32s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 46s. +[2025-08-20 13:31:25,583][__main__][INFO] - Starting iteration 342. +[2025-08-20 13:31:48,979][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:31:48,980][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:31:48,987][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:31:51,459][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:31:51,461][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:31:51,467][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:31:51,470][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:31:51,470][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:31:51,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:52,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:53,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:54,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:54,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:55,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:56,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:57,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:58,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:58,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:31:59,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:00,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:01,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:02,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:02,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:03,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:04,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:05,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:06,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:06,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:08,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:08,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:09,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:10,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:11,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:12,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:12,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:13,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:14,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:15,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:16,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:16,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:18,483][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:32:19,435][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:32:19,436][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:32:20,841][__main__][INFO] - Iteration 343 took 55s (37.87% Gen, 62.13% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 59m 7s. Estimated total time: 15h 20m 57s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 5s, 500 more iterations: 7h 40m 28s. +[2025-08-20 13:32:20,842][__main__][INFO] - Starting iteration 343. +[2025-08-20 13:32:44,464][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:32:44,466][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:32:44,472][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:32:46,913][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:32:46,914][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:32:46,920][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:32:46,923][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:32:46,923][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:32:47,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:48,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:48,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:49,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:50,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:51,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:51,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:52,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:53,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:54,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:55,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:55,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:56,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:57,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:58,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:59,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:32:59,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:00,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:01,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:02,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:03,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:03,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:04,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:05,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:06,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:07,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:08,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:09,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:09,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:10,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:11,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:12,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:13,817][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:33:14,781][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:33:14,783][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:33:16,160][__main__][INFO] - Iteration 344 took 55s (38.28% Gen, 61.72% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 59m 11s. Estimated total time: 15h 21m 56s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 58s. +[2025-08-20 13:33:16,161][__main__][INFO] - Starting iteration 344. +[2025-08-20 13:33:39,233][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:33:39,235][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:33:39,241][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:33:41,697][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:33:41,698][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:33:41,705][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:33:41,707][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:33:41,708][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:33:42,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:42,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:43,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:44,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:45,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:45,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:46,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:47,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:48,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:49,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:49,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:50,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:51,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:52,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:53,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:53,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:54,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:55,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:56,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:57,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:57,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:58,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:33:59,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:00,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:01,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:02,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:03,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:03,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:04,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:05,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:06,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:07,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:08,719][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:34:09,686][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:34:09,688][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:34:11,025][__main__][INFO] - Iteration 345 took 54s (37.61% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 50m 43s. Estimated total time: 15h 14m 23s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 11s. +[2025-08-20 13:34:11,026][__main__][INFO] - Starting iteration 345. +[2025-08-20 13:34:34,149][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:34:34,151][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:34:34,157][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:34:36,627][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:34:38,384][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:34:38,393][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:34:38,396][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:34:38,397][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:34:38,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:39,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:40,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:41,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:41,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:42,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:43,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:44,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:45,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:45,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:46,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:48,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:50,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:50,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:51,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:52,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:53,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:54,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:54,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:55,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:56,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:57,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:58,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:34:59,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:00,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:00,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:01,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:02,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:03,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:04,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:04,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:05,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:07,244][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:35:08,181][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:35:08,182][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:35:09,553][__main__][INFO] - Iteration 346 took 58s (35.32% Gen, 64.68% Train). Generation: 20s, Training: 37s. Estimated remaining time: 10h 50m 48s. Estimated total time: 16h 15m 26s. Time estimates for 10 more iterations: 9m 45s, 100 more iterations: 1h 37m 32s, 500 more iterations: 8h 7m 43s. +[2025-08-20 13:35:09,555][__main__][INFO] - Starting iteration 346. +[2025-08-20 13:35:32,850][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:35:32,852][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:35:32,858][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:35:35,324][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:35:35,325][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:35:35,331][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:35:35,334][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:35:35,335][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:35:35,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:36,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:37,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:38,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:38,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:39,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:40,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:41,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:41,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:42,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:43,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:44,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:45,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:45,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:46,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:47,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:48,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:49,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:49,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:50,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:51,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:52,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:53,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:54,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:55,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:55,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:56,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:57,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:58,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:59,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:35:59,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:00,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:02,338][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:36:03,424][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:36:03,427][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:36:04,790][__main__][INFO] - Iteration 347 took 55s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 55m 1s. Estimated total time: 15h 20m 34s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 3s, 500 more iterations: 7h 40m 17s. +[2025-08-20 13:36:04,792][__main__][INFO] - Starting iteration 347. +[2025-08-20 13:36:27,782][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:36:27,787][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:36:27,796][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:36:30,275][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:36:30,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:36:30,283][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:36:30,285][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:36:30,286][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:36:30,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:31,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:32,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:32,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:33,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:34,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:35,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:36,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:36,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:37,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:38,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:39,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:40,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:40,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:41,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:42,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:43,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:44,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:45,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:46,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:46,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:47,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:48,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:49,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:50,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:50,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:51,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:52,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:53,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:54,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:54,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:55,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:36:57,324][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:37:00,119][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:37:00,123][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:37:01,596][__main__][INFO] - Iteration 348 took 56s (36.13% Gen, 63.87% Train). Generation: 20s, Training: 36s. Estimated remaining time: 10h 20m 12s. Estimated total time: 15h 46m 42s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 40s, 500 more iterations: 7h 53m 21s. +[2025-08-20 13:37:01,598][__main__][INFO] - Starting iteration 348. +[2025-08-20 13:37:25,221][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:37:25,222][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:37:25,228][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:37:27,661][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:37:27,663][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:37:27,669][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:37:27,671][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:37:27,672][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:37:27,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:28,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:29,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:30,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:31,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:31,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:32,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:33,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:34,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:35,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:35,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:36,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:37,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:38,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:39,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:39,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:40,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:41,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:42,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:43,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:43,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:44,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:45,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:46,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:47,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:48,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:49,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:49,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:50,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:51,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:52,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:53,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:37:54,592][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:37:55,551][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:37:55,552][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:37:57,821][__main__][INFO] - Iteration 349 took 56s (37.66% Gen, 62.34% Train). Generation: 21s, Training: 35s. Estimated remaining time: 10h 9m 18s. Estimated total time: 15h 36m 45s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 40s, 500 more iterations: 7h 48m 22s. +[2025-08-20 13:37:57,823][__main__][INFO] - Starting iteration 349. +[2025-08-20 13:38:20,919][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:38:20,920][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:38:20,927][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:38:23,371][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:38:23,373][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:38:23,379][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:38:23,381][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:38:23,382][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:38:23,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:24,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:25,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:26,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:26,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:27,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:28,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:29,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:30,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:30,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:31,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:32,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:33,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:33,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:34,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:35,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:36,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:37,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:38,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:39,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:39,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:40,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:41,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:42,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:43,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:43,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:44,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:45,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:46,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:47,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:47,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:48,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:38:50,284][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:38:51,228][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:38:51,230][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:38:52,577][__main__][INFO] - Iteration 350 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 44m 11s. Estimated total time: 15h 12m 32s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 16s. +[2025-08-20 13:38:52,579][__main__][INFO] - Starting iteration 350. +[2025-08-20 13:39:17,351][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:39:17,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:39:17,359][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:39:19,813][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:39:19,815][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:39:19,821][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:39:19,823][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:39:19,824][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:39:20,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:20,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:21,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:22,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:23,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:24,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:24,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:25,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:26,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:27,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:28,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:28,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:29,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:30,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:31,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:32,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:32,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:33,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:34,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:35,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:36,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:37,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:38,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:38,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:39,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:40,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:41,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:42,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:42,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:43,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:44,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:45,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:39:46,795][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:39:47,747][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:39:47,748][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:39:51,972][__main__][INFO] - Iteration 351 took 59s (37.56% Gen, 57.71% Train). Generation: 22s, Training: 34s. Estimated remaining time: 11h 0m 32s. Estimated total time: 16h 29m 53s. Time estimates for 10 more iterations: 9m 53s, 100 more iterations: 1h 38m 59s, 500 more iterations: 8h 14m 56s. +[2025-08-20 13:39:51,974][__main__][INFO] - Starting iteration 351. +[2025-08-20 13:40:15,457][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:40:15,458][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:40:15,465][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:40:17,904][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:40:17,905][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:40:17,912][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:40:17,914][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:40:17,914][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:40:18,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:19,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:19,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:20,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:21,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:22,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:22,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:23,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:24,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:25,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:26,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:26,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:27,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:28,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:29,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:30,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:30,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:31,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:32,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:33,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:34,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:35,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:36,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:36,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:37,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:38,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:39,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:40,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:40,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:41,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:42,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:43,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:40:44,842][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:40:45,780][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:40:45,781][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:40:47,487][__main__][INFO] - Iteration 352 took 55s (37.92% Gen, 62.08% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 54m 55s. Estimated total time: 15h 25m 12s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 31s, 500 more iterations: 7h 42m 36s. +[2025-08-20 13:40:47,488][__main__][INFO] - Starting iteration 352. +[2025-08-20 13:41:10,800][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:41:10,802][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:41:10,808][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:41:13,244][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:41:13,246][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:41:13,252][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:41:13,254][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:41:13,255][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:41:13,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:14,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:15,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:15,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:16,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:17,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:18,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:19,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:19,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:20,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:21,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:22,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:23,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:23,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:24,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:25,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:26,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:27,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:27,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:28,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:29,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:30,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:31,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:32,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:33,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:33,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:34,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:35,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:36,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:37,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:37,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:38,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:41:40,197][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:41:41,143][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:41:41,145][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:41:42,504][__main__][INFO] - Iteration 353 took 55s (37.94% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 45m 44s. Estimated total time: 15h 16m 55s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 27s. +[2025-08-20 13:41:42,506][__main__][INFO] - Starting iteration 353. +[2025-08-20 13:42:06,006][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:42:06,008][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:42:06,014][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:42:08,453][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:42:08,455][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:42:08,461][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:42:08,463][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:42:08,464][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:42:08,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:09,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:10,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:11,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:11,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:12,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:13,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:14,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:15,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:15,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:16,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:17,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:18,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:19,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:19,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:20,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:21,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:22,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:23,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:24,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:25,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:25,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:26,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:27,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:28,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:29,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:29,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:30,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:31,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:32,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:33,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:33,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:42:35,486][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:42:36,440][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:42:36,441][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:42:37,794][__main__][INFO] - Iteration 354 took 55s (38.10% Gen, 61.90% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 49m 20s. Estimated total time: 15h 21m 27s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 8s, 500 more iterations: 7h 40m 43s. +[2025-08-20 13:42:37,795][__main__][INFO] - Starting iteration 354. +[2025-08-20 13:43:01,266][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:01,267][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:01,274][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:03,744][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:03,746][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:03,752][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:03,754][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:43:03,755][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:43:04,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:04,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:05,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:06,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:07,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:08,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:08,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:09,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:10,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:11,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:11,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:12,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:13,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:14,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:15,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:15,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:16,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:17,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:18,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:19,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:19,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:20,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:21,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:22,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:23,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:23,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:25,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:25,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:26,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:27,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:28,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:29,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:30,708][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:43:31,680][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:43:31,682][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:43:33,014][__main__][INFO] - Iteration 355 took 55s (38.04% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 47m 16s. Estimated total time: 15h 20m 18s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 1s, 500 more iterations: 7h 40m 9s. +[2025-08-20 13:43:33,015][__main__][INFO] - Starting iteration 355. +[2025-08-20 13:43:56,061][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:56,062][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:56,068][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:58,533][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:58,535][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:58,541][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:43:58,543][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:43:58,544][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:43:58,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:43:59,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:00,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:01,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:02,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:02,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:03,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:04,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:05,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:05,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:06,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:07,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:08,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:09,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:09,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:10,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:11,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:12,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:13,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:14,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:15,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:15,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:16,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:17,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:18,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:19,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:19,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:20,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:21,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:22,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:23,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:23,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:25,499][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:44:26,455][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:44:26,458][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:44:28,009][__main__][INFO] - Iteration 356 took 54s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 42m 35s. Estimated total time: 15h 16m 32s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 16s. +[2025-08-20 13:44:28,010][__main__][INFO] - Starting iteration 356. +[2025-08-20 13:44:51,264][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:44:51,265][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:44:51,272][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:44:53,720][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:44:53,722][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:44:53,728][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:44:53,731][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:44:53,731][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:44:54,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:54,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:55,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:56,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:57,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:57,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:58,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:44:59,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:00,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:01,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:01,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:02,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:03,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:04,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:05,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:05,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:06,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:07,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:08,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:09,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:09,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:10,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:12,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:12,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:13,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:14,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:15,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:15,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:16,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:17,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:18,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:19,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:20,709][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:45:21,658][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:45:21,660][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:45:22,970][__main__][INFO] - Iteration 357 took 54s (37.85% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 41m 7s. Estimated total time: 15h 15m 59s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 59s. +[2025-08-20 13:45:22,972][__main__][INFO] - Starting iteration 357. +[2025-08-20 13:45:47,031][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:45:47,032][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:45:47,038][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:45:49,507][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:45:49,509][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:45:49,515][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:45:49,517][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:45:49,518][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:45:49,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:50,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:51,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:52,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:52,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:53,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:54,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:55,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:56,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:56,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:57,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:58,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:45:59,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:00,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:00,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:01,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:02,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:03,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:04,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:04,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:05,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:06,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:07,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:08,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:08,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:09,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:10,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:11,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:12,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:13,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:14,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:14,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:16,457][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:46:17,418][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:46:17,420][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:46:18,792][__main__][INFO] - Iteration 358 took 55s (38.70% Gen, 61.30% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 54m 32s. Estimated total time: 15h 30m 19s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 1s, 500 more iterations: 7h 45m 9s. +[2025-08-20 13:46:18,794][__main__][INFO] - Starting iteration 358. +[2025-08-20 13:46:42,266][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:46:42,267][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:46:42,273][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:46:44,726][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:46:44,727][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:46:44,733][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:46:44,736][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:46:44,736][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:46:45,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:45,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:46,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:47,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:48,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:49,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:49,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:50,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:51,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:52,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:52,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:53,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:54,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:55,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:56,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:56,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:57,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:58,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:46:59,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:00,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:00,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:02,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:02,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:03,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:04,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:05,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:06,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:06,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:07,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:08,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:09,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:10,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:11,727][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:47:12,680][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:47:12,682][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:47:14,138][__main__][INFO] - Iteration 359 took 55s (38.00% Gen, 62.00% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 45m 40s. Estimated total time: 15h 22m 23s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 11s. +[2025-08-20 13:47:14,139][__main__][INFO] - Starting iteration 359. +[2025-08-20 13:47:37,713][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:47:37,715][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:47:37,721][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:47:40,210][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:47:40,212][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:47:40,218][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:47:40,220][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:47:40,221][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:47:40,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:41,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:42,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:42,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:43,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:44,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:45,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:46,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:46,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:47,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:48,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:49,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:50,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:50,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:51,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:52,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:53,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:53,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:54,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:55,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:56,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:57,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:57,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:47:58,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:00,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:00,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:01,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:02,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:03,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:03,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:04,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:05,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:07,214][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:48:08,174][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:48:08,175][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:48:09,420][__main__][INFO] - Iteration 360 took 55s (38.17% Gen, 61.83% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 43m 41s. Estimated total time: 15h 21m 19s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 7s, 500 more iterations: 7h 40m 39s. +[2025-08-20 13:48:09,421][__main__][INFO] - Starting iteration 360. +[2025-08-20 13:48:32,655][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:48:32,656][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:48:32,662][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:48:35,120][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:48:35,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:48:35,128][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:48:35,130][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:48:35,131][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:48:35,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:36,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:37,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:37,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:38,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:39,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:40,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:40,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:41,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:42,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:43,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:44,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:44,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:45,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:46,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:47,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:48,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:48,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:49,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:50,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:51,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:52,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:53,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:54,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:54,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:55,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:56,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:57,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:58,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:58,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:48:59,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:00,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:02,102][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:49:03,189][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:49:03,191][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:49:04,620][__main__][INFO] - Iteration 361 took 55s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 41m 24s. Estimated total time: 15h 19m 58s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 59s, 500 more iterations: 7h 39m 59s. +[2025-08-20 13:49:04,622][__main__][INFO] - Starting iteration 361. +[2025-08-20 13:49:27,835][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:49:27,836][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:49:27,843][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:49:30,297][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:49:30,298][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:49:30,305][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:49:30,307][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:49:30,307][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:49:30,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:31,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:32,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:32,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:33,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:34,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:35,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:36,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:36,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:37,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:38,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:39,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:40,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:40,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:41,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:42,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:43,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:44,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:44,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:45,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:46,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:47,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:48,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:48,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:49,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:50,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:51,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:52,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:53,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:54,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:54,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:55,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:49:57,281][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:49:58,199][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:49:58,201][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:49:59,568][__main__][INFO] - Iteration 362 took 54s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 36m 17s. Estimated total time: 15h 15m 46s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 34s, 500 more iterations: 7h 37m 53s. +[2025-08-20 13:49:59,570][__main__][INFO] - Starting iteration 362. +[2025-08-20 13:50:22,583][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:50:22,584][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:50:22,590][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:50:25,059][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:50:25,060][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:50:25,067][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:50:25,069][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:50:25,070][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:50:25,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:26,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:26,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:27,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:28,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:29,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:30,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:30,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:31,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:32,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:33,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:34,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:34,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:35,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:36,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:37,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:38,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:38,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:39,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:40,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:41,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:42,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:42,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:43,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:44,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:45,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:46,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:47,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:48,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:48,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:49,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:50,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:50:52,129][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:50:53,068][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:50:53,070][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:50:54,656][__main__][INFO] - Iteration 363 took 55s (37.33% Gen, 62.67% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 37m 42s. Estimated total time: 15h 18m 6s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 3s. +[2025-08-20 13:50:54,658][__main__][INFO] - Starting iteration 363. +[2025-08-20 13:51:17,715][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:51:17,716][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:51:17,722][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:51:20,173][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:51:20,174][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:51:20,181][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:51:20,183][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:51:20,183][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:51:20,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:21,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:22,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:22,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:23,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:24,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:25,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:26,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:26,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:27,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:28,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:29,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:30,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:30,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:31,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:32,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:33,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:33,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:34,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:35,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:36,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:37,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:38,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:39,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:39,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:40,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:41,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:42,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:43,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:43,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:44,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:45,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:51:47,210][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:51:48,166][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:51:48,167][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:51:49,511][__main__][INFO] - Iteration 364 took 54s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 32m 53s. Estimated total time: 15h 14m 12s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 25s, 500 more iterations: 7h 37m 6s. +[2025-08-20 13:51:49,512][__main__][INFO] - Starting iteration 364. +[2025-08-20 13:52:12,481][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:52:12,483][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:52:12,489][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:52:14,969][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:52:14,970][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:52:14,977][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:52:14,979][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:52:14,979][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:52:15,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:16,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:16,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:17,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:18,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:19,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:20,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:20,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:21,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:22,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:23,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:24,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:24,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:25,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:26,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:27,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:27,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:29,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:30,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:30,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:31,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:32,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:33,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:33,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:34,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:35,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:36,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:37,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:37,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:38,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:39,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:40,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:52:41,978][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:52:42,914][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:52:42,915][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:52:46,186][__main__][INFO] - Iteration 365 took 56s (36.15% Gen, 63.85% Train). Generation: 20s, Training: 36s. Estimated remaining time: 10h 2m 18s. Estimated total time: 15h 44m 33s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 27s, 500 more iterations: 7h 52m 16s. +[2025-08-20 13:52:46,187][__main__][INFO] - Starting iteration 365. +[2025-08-20 13:53:09,509][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:53:09,510][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:53:09,516][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:53:11,976][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:53:11,977][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:53:11,983][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:53:11,985][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:53:11,986][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:53:12,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:13,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:13,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:14,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:15,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:16,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:17,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:17,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:18,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:19,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:20,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:21,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:21,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:22,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:23,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:24,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:25,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:26,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:27,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:27,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:28,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:29,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:30,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:31,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:31,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:32,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:33,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:34,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:34,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:35,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:36,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:37,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:53:39,031][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:53:39,980][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:53:39,982][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:53:41,310][__main__][INFO] - Iteration 366 took 55s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 35m 32s. Estimated total time: 15h 18m 42s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 21s. +[2025-08-20 13:53:41,312][__main__][INFO] - Starting iteration 366. +[2025-08-20 13:54:04,385][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:54:04,387][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:54:04,393][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:54:06,843][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:54:06,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:54:06,851][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:54:06,853][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:54:06,854][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:54:07,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:07,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:08,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:09,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:10,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:11,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:11,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:13,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:13,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:15,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:18,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:19,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:20,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:20,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:21,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:22,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:23,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:23,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:24,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:25,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:26,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:27,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:27,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:28,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:29,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:30,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:31,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:34,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:34,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:35,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:36,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:37,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:54:38,991][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:32, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:54:39,934][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:54:39,936][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:54:41,369][__main__][INFO] - Iteration 367 took 1m 0s (34.35% Gen, 65.65% Train). Generation: 20s, Training: 39s. Estimated remaining time: 10h 56m 46s. Estimated total time: 16h 40m 56s. Time estimates for 10 more iterations: 10m 0s, 100 more iterations: 1h 40m 5s, 500 more iterations: 8h 20m 28s. +[2025-08-20 13:54:41,370][__main__][INFO] - Starting iteration 367. +[2025-08-20 13:55:04,371][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:04,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:04,378][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:06,822][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:06,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:06,831][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:06,833][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:55:06,834][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:55:07,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:07,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:08,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:09,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:10,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:11,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:11,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:12,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:13,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:14,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:15,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:15,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:16,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:17,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:18,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:19,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:19,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:21,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:21,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:22,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:23,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:24,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:25,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:25,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:26,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:27,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:28,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:29,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:29,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:30,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:31,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:32,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:55:33,911][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:55:34,905][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:55:34,909][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:55:36,361][__main__][INFO] - Iteration 368 took 54s (37.38% Gen, 62.62% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 31m 25s. Estimated total time: 15h 16m 30s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 15s. +[2025-08-20 13:55:36,363][__main__][INFO] - Starting iteration 368. +[2025-08-20 13:55:59,347][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:59,349][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:55:59,355][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:01,848][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:01,849][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:01,855][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:01,858][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:56:01,858][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:56:02,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:02,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:03,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:04,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:05,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:06,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:06,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:07,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:08,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:09,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:10,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:10,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:11,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:12,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:13,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:14,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:14,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:15,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:16,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:17,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:18,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:19,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:20,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:20,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:21,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:22,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:23,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:24,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:24,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:25,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:26,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:27,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:56:28,945][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:56:29,924][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:56:29,926][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:56:33,746][__main__][INFO] - Iteration 369 took 57s (35.71% Gen, 64.29% Train). Generation: 20s, Training: 36s. Estimated remaining time: 10h 10m 19s. Estimated total time: 15h 56m 22s. Time estimates for 10 more iterations: 9m 33s, 100 more iterations: 1h 35m 38s, 500 more iterations: 7h 58m 11s. +[2025-08-20 13:56:33,747][__main__][INFO] - Starting iteration 369. +[2025-08-20 13:56:56,674][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:56,675][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:56,681][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:59,144][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:59,146][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:59,152][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:56:59,154][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:56:59,156][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:56:59,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:00,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:01,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:01,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:02,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:03,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:04,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:04,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:05,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:06,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:07,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:08,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:08,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:09,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:10,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:11,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:12,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:13,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:14,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:14,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:15,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:16,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:17,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:18,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:18,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:19,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:20,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:21,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:22,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:22,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:23,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:24,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:26,091][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:57:27,099][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:57:27,101][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:57:28,626][__main__][INFO] - Iteration 370 took 54s (37.28% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 27m 40s. Estimated total time: 15h 14m 38s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 19s. +[2025-08-20 13:57:28,628][__main__][INFO] - Starting iteration 370. +[2025-08-20 13:57:52,353][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:57:52,354][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:57:52,361][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:57:54,822][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:57:54,823][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:57:54,830][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:57:54,832][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:57:54,833][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:57:55,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:55,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:56,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:57,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:58,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:59,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:57:59,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:00,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:01,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:02,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:03,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:03,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:04,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:05,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:06,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:07,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:07,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:08,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:09,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:10,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:10,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:11,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:12,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:13,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:14,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:15,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:16,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:17,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:17,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:18,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:19,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:20,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:21,893][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:58:22,953][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:58:22,955][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:58:24,368][__main__][INFO] - Iteration 371 took 55s (38.17% Gen, 61.83% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 41m 6s. Estimated total time: 15h 28m 59s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 53s, 500 more iterations: 7h 44m 29s. +[2025-08-20 13:58:24,369][__main__][INFO] - Starting iteration 371. +[2025-08-20 13:58:47,488][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:58:47,490][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:58:47,496][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:58:49,967][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:58:49,968][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:58:49,974][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:58:49,976][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:58:49,977][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:58:50,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:51,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:51,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:52,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:53,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:54,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:55,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:55,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:56,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:57,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:58,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:59,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:58:59,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:00,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:01,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:02,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:02,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:03,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:04,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:05,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:06,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:07,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:08,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:09,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:09,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:10,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:11,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:12,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:13,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:13,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:14,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:15,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:17,164][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 13:59:18,114][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 13:59:18,115][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 13:59:19,440][__main__][INFO] - Iteration 372 took 55s (37.52% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 29m 2s. Estimated total time: 15h 17m 50s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 55s. +[2025-08-20 13:59:19,442][__main__][INFO] - Starting iteration 372. +[2025-08-20 13:59:42,504][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:59:42,505][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:59:42,512][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:59:44,962][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:59:44,963][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:59:44,970][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 13:59:44,972][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 13:59:44,972][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 13:59:45,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:46,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:46,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:47,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:48,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:49,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:50,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:50,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:51,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:52,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:53,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:54,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:54,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:55,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:56,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:57,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:57,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:58,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 13:59:59,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:00,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:01,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:02,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:03,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:04,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:04,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:05,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:06,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:07,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:08,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:08,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:09,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:10,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:12,028][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:00:13,060][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:00:13,062][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:00:14,483][__main__][INFO] - Iteration 373 took 55s (37.43% Gen, 62.57% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 27m 36s. Estimated total time: 15h 17m 20s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 44s, 500 more iterations: 7h 38m 40s. +[2025-08-20 14:00:14,484][__main__][INFO] - Starting iteration 373. +[2025-08-20 14:00:37,541][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:00:37,543][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:00:37,549][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:00:40,013][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:00:40,014][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:00:40,020][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:00:40,022][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:00:40,023][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:00:40,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:41,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:41,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:42,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:43,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:44,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:45,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:45,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:46,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:47,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:48,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:49,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:49,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:50,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:51,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:52,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:53,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:53,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:55,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:55,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:56,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:57,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:58,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:59,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:00:59,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:00,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:01,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:02,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:03,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:03,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:04,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:05,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:07,047][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:01:07,982][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:01:07,984][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:01:09,355][__main__][INFO] - Iteration 374 took 54s (37.53% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 23m 51s. Estimated total time: 15h 14m 30s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 15s. +[2025-08-20 14:01:09,356][__main__][INFO] - Starting iteration 374. +[2025-08-20 14:01:32,443][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:01:32,444][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:01:32,450][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:01:34,922][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:01:34,923][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:01:34,930][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:01:34,932][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:01:34,932][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:01:35,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:36,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:36,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:37,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:38,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:39,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:39,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:40,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:41,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:42,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:43,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:43,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:44,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:45,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:46,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:47,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:47,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:48,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:50,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:50,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:51,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:52,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:53,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:54,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:54,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:55,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:56,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:57,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:57,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:58,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:01:59,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:00,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:02,004][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:02:02,946][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:02:02,947][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:02:04,222][__main__][INFO] - Iteration 375 took 54s (37.60% Gen, 62.40% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 22m 52s. Estimated total time: 15h 14m 25s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 12s. +[2025-08-20 14:02:04,224][__main__][INFO] - Starting iteration 375. +[2025-08-20 14:02:27,876][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:02:27,877][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:02:27,883][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:02:30,326][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:02:30,328][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:02:30,334][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:02:30,336][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:02:30,337][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:02:30,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:31,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:32,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:33,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:33,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:34,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:35,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:36,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:36,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:37,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:38,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:39,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:40,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:40,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:41,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:42,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:43,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:44,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:44,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:46,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:46,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:47,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:48,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:49,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:50,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:50,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:51,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:52,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:53,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:54,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:54,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:55,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:02:57,297][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:02:58,251][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:02:58,252][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:02:59,651][__main__][INFO] - Iteration 376 took 55s (38.27% Gen, 61.72% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 31m 18s. Estimated total time: 15h 23m 47s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 22s, 500 more iterations: 7h 41m 53s. +[2025-08-20 14:02:59,653][__main__][INFO] - Starting iteration 376. +[2025-08-20 14:03:22,940][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:03:22,941][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:03:22,947][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:03:25,403][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:03:25,404][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:03:25,410][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:03:25,412][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:03:25,413][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:03:25,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:26,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:27,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:28,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:28,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:29,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:30,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:31,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:32,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:32,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:33,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:34,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:35,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:36,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:36,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:37,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:38,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:39,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:40,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:40,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:41,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:42,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:43,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:44,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:45,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:46,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:46,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:47,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:48,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:49,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:50,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:50,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:03:52,435][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:03:53,402][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:03:53,403][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:03:54,752][__main__][INFO] - Iteration 377 took 55s (37.80% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 24m 55s. Estimated total time: 15h 18m 19s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 9s. +[2025-08-20 14:03:54,757][__main__][INFO] - Starting iteration 377. +[2025-08-20 14:04:17,769][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:04:17,770][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:04:17,777][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:04:20,206][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:04:20,207][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:04:20,213][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:04:20,215][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:04:20,216][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:04:20,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:21,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:22,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:22,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:23,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:24,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:25,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:26,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:26,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:27,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:28,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:29,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:30,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:30,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:31,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:32,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:33,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:33,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:34,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:36,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:36,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:37,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:38,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:39,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:40,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:40,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:41,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:42,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:43,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:44,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:44,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:45,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:04:47,288][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:04:48,210][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:04:48,212][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:04:49,591][__main__][INFO] - Iteration 378 took 54s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 19m 35s. Estimated total time: 15h 13m 53s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 23s, 500 more iterations: 7h 36m 56s. +[2025-08-20 14:04:49,593][__main__][INFO] - Starting iteration 378. +[2025-08-20 14:05:12,549][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:05:12,551][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:05:12,557][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:05:15,018][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:05:15,019][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:05:15,026][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:05:15,028][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:05:15,029][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:05:15,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:16,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:16,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:17,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:18,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:19,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:20,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:20,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:21,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:22,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:23,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:24,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:24,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:25,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:26,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:27,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:28,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:29,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:30,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:30,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:31,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:32,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:33,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:34,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:34,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:35,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:36,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:37,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:37,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:38,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:39,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:40,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:05:42,025][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:05:42,973][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:05:42,974][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:05:44,540][__main__][INFO] - Iteration 379 took 54s (37.31% Gen, 62.69% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 20m 33s. Estimated total time: 15h 15m 47s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 34s, 500 more iterations: 7h 37m 53s. +[2025-08-20 14:05:44,542][__main__][INFO] - Starting iteration 379. +[2025-08-20 14:06:07,613][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:06:07,615][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:06:07,621][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:06:10,060][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:06:10,062][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:06:10,068][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:06:10,070][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:06:10,071][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:06:10,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:11,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:11,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:12,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:13,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:14,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:15,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:15,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:16,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:17,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:18,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:19,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:19,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:20,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:21,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:22,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:23,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:23,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:25,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:25,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:26,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:27,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:28,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:29,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:29,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:30,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:31,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:32,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:33,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:33,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:34,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:35,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:06:37,045][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:06:38,021][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:06:38,022][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:06:39,462][__main__][INFO] - Iteration 380 took 54s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 19m 11s. Estimated total time: 15h 15m 20s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 40s. +[2025-08-20 14:06:39,470][__main__][INFO] - Starting iteration 380. +[2025-08-20 14:07:02,853][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:02,854][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:02,861][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:05,332][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:05,333][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:05,340][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:05,342][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:07:05,343][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:07:05,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:06,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:07,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:08,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:08,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:09,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:10,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:11,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:11,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:12,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:13,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:14,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:15,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:15,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:16,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:17,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:18,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:19,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:19,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:20,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:21,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:22,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:23,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:24,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:25,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:26,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:26,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:27,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:28,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:29,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:30,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:30,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:07:32,426][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:07:33,335][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:07:33,336][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:07:34,621][__main__][INFO] - Iteration 381 took 55s (37.92% Gen, 62.08% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 22m 7s. Estimated total time: 15h 19m 11s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 35s. +[2025-08-20 14:07:34,623][__main__][INFO] - Starting iteration 381. +[2025-08-20 14:07:57,593][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:57,594][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:07:57,600][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:00,048][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:00,049][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:00,056][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:00,058][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:08:00,059][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:08:00,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:01,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:01,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:02,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:03,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:04,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:05,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:05,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:06,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:07,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:08,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:09,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:09,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:10,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:11,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:12,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:13,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:13,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:14,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:15,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:16,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:17,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:18,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:19,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:19,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:20,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:21,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:22,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:23,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:23,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:24,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:25,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:27,017][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:08:27,982][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:08:27,984][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:08:29,348][__main__][INFO] - Iteration 382 took 54s (37.51% Gen, 62.49% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 14m 6s. Estimated total time: 15h 12m 4s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 12s, 500 more iterations: 7h 36m 2s. +[2025-08-20 14:08:29,349][__main__][INFO] - Starting iteration 382. +[2025-08-20 14:08:52,323][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:52,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:52,331][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:54,797][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:54,799][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:54,805][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:08:54,807][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:08:54,808][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:08:55,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:55,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:56,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:57,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:58,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:59,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:08:59,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:00,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:01,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:02,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:03,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:03,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:04,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:05,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:06,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:07,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:07,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:08,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:09,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:10,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:10,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:11,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:12,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:13,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:14,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:15,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:16,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:17,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:17,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:18,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:19,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:20,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:21,966][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:09:22,947][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:09:22,949][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:09:24,322][__main__][INFO] - Iteration 383 took 54s (37.31% Gen, 62.69% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 17m 19s. Estimated total time: 15h 16m 12s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 6s. +[2025-08-20 14:09:24,324][__main__][INFO] - Starting iteration 383. +[2025-08-20 14:09:47,348][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:09:47,349][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:09:47,355][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:09:49,822][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:09:49,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:09:49,830][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:09:49,832][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:09:49,833][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:09:50,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:50,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:51,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:52,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:53,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:54,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:54,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:55,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:56,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:57,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:58,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:58,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:09:59,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:00,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:01,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:02,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:02,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:03,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:04,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:05,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:06,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:07,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:08,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:08,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:09,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:10,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:11,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:12,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:12,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:13,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:14,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:15,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:16,944][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:10:17,949][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:10:17,951][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:10:19,376][__main__][INFO] - Iteration 384 took 55s (37.41% Gen, 62.58% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 17m 43s. Estimated total time: 15h 17m 31s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 45s. +[2025-08-20 14:10:19,377][__main__][INFO] - Starting iteration 384. +[2025-08-20 14:10:42,518][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:10:42,520][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:10:42,526][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:10:45,014][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:10:45,016][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:10:45,022][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:10:45,024][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:10:45,025][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:10:45,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:46,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:46,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:47,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:48,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:49,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:50,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:50,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:51,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:52,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:53,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:54,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:54,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:55,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:56,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:57,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:58,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:10:59,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:00,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:00,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:01,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:02,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:03,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:04,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:04,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:05,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:06,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:07,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:08,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:08,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:09,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:10,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:12,021][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:11:12,958][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:11:12,959][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:11:14,408][__main__][INFO] - Iteration 385 took 55s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 16m 26s. Estimated total time: 15h 17m 10s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 35s. +[2025-08-20 14:11:14,410][__main__][INFO] - Starting iteration 385. +[2025-08-20 14:11:37,443][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:11:37,444][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:11:37,450][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:11:39,901][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:11:39,903][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:11:39,909][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:11:39,911][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:11:39,912][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:11:40,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:41,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:41,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:42,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:43,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:44,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:44,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:45,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:46,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:47,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:48,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:48,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:49,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:50,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:51,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:52,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:52,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:53,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:54,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:55,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:56,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:57,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:58,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:58,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:11:59,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:00,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:01,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:02,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:02,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:03,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:04,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:05,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:06,967][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:12:07,996][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:12:07,998][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:12:09,494][__main__][INFO] - Iteration 386 took 55s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 16m 25s. Estimated total time: 15h 18m 4s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 2s. +[2025-08-20 14:12:09,496][__main__][INFO] - Starting iteration 386. +[2025-08-20 14:12:33,004][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:12:33,005][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:12:33,012][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:12:35,439][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:12:35,441][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:12:35,447][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:12:35,450][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:12:35,450][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:12:35,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:36,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:37,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:38,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:38,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:39,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:40,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:41,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:42,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:42,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:43,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:44,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:45,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:46,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:46,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:47,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:48,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:49,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:50,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:51,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:52,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:52,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:53,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:54,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:55,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:56,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:56,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:57,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:58,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:12:59,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:00,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:00,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:02,473][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:13:03,486][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:13:03,489][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:13:04,837][__main__][INFO] - Iteration 387 took 55s (38.10% Gen, 61.90% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 19m 46s. Estimated total time: 15h 22m 20s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 10s. +[2025-08-20 14:13:04,839][__main__][INFO] - Starting iteration 387. +[2025-08-20 14:13:27,897][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:13:27,898][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:13:27,904][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:13:30,369][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:13:30,371][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:13:30,377][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:13:30,379][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:13:30,380][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:13:30,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:31,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:32,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:33,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:33,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:34,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:35,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:36,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:37,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:37,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:38,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:39,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:40,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:41,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:41,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:42,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:43,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:44,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:44,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:45,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:46,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:47,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:48,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:48,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:50,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:50,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:51,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:52,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:53,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:54,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:54,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:55,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:13:57,403][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:13:58,447][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:13:58,449][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:14:01,938][__main__][INFO] - Iteration 388 took 57s (36.08% Gen, 63.92% Train). Generation: 20s, Training: 36s. Estimated remaining time: 9h 48m 8s. Estimated total time: 15h 51m 39s. Time estimates for 10 more iterations: 9m 30s, 100 more iterations: 1h 35m 9s, 500 more iterations: 7h 55m 49s. +[2025-08-20 14:14:01,940][__main__][INFO] - Starting iteration 388. +[2025-08-20 14:14:26,634][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:14:26,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:14:26,642][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:14:29,097][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:14:29,098][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:14:29,105][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:14:29,107][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:14:29,108][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:14:29,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:30,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:30,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:31,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:32,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:33,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:34,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:34,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:35,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:36,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:37,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:38,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:38,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:39,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:40,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:41,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:42,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:43,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:44,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:45,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:45,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:46,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:47,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:48,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:48,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:49,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:50,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:51,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:52,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:52,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:53,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:54,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:14:56,211][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:14:57,239][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:14:57,242][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:14:58,640][__main__][INFO] - Iteration 389 took 56s (39.19% Gen, 60.81% Train). Generation: 22s, Training: 34s. Estimated remaining time: 9h 40m 31s. Estimated total time: 15h 44m 59s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 29s, 500 more iterations: 7h 52m 29s. +[2025-08-20 14:14:58,641][__main__][INFO] - Starting iteration 389. +[2025-08-20 14:15:21,650][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:15:21,651][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:15:21,658][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:15:24,123][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:15:24,124][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:15:24,130][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:15:24,132][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:15:24,133][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:15:24,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:25,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:26,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:26,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:27,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:28,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:29,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:29,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:30,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:31,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:32,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:33,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:33,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:34,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:35,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:36,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:37,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:38,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:39,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:39,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:40,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:41,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:42,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:43,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:43,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:44,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:45,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:46,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:47,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:47,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:48,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:49,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:15:51,142][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:15:52,237][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:15:52,239][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:15:54,059][__main__][INFO] - Iteration 390 took 55s (37.10% Gen, 62.90% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 18m 14s. Estimated total time: 15h 23m 37s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 21s, 500 more iterations: 7h 41m 48s. +[2025-08-20 14:15:54,061][__main__][INFO] - Starting iteration 390. +[2025-08-20 14:16:17,059][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:16:17,060][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:16:17,066][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:16:19,525][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:16:19,527][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:16:19,533][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:16:19,535][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:16:19,536][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:16:19,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:20,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:21,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:22,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:23,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:23,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:24,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:25,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:26,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:26,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:27,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:28,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:29,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:30,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:30,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:31,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:32,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:33,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:34,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:35,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:36,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:37,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:37,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:38,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:39,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:40,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:40,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:41,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:42,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:43,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:44,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:44,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:16:46,584][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:16:47,461][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:16:47,463][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:16:48,950][__main__][INFO] - Iteration 391 took 54s (37.43% Gen, 62.57% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 8m 31s. Estimated total time: 15h 14m 49s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 24s. +[2025-08-20 14:16:48,952][__main__][INFO] - Starting iteration 391. +[2025-08-20 14:17:12,276][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:17:12,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:17:12,283][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:17:14,740][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:17:14,742][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:17:14,748][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:17:14,750][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:17:14,751][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:17:15,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:15,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:16,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:17,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:18,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:19,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:19,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:20,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:21,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:22,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:22,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:23,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:24,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:25,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:26,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:26,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:27,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:28,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:29,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:30,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:30,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:31,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:32,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:33,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:34,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:34,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:35,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:36,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:37,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:38,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:39,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:40,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:17:41,827][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:17:42,749][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:17:42,750][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:17:44,021][__main__][INFO] - Iteration 392 took 55s (37.92% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 10m 36s. Estimated total time: 15h 17m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 54s. +[2025-08-20 14:17:44,023][__main__][INFO] - Starting iteration 392. +[2025-08-20 14:18:06,913][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:18:06,914][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:18:06,920][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:18:09,361][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:18:09,362][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:18:09,369][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:18:09,371][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:18:09,372][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:18:09,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:10,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:11,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:12,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:12,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:13,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:14,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:15,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:16,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:16,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:17,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:18,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:19,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:19,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:20,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:21,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:22,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:23,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:23,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:25,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:26,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:26,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:27,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:28,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:29,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:29,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:30,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:31,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:32,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:33,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:33,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:34,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:18:36,416][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:18:37,355][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:18:37,357][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:18:38,782][__main__][INFO] - Iteration 393 took 54s (37.35% Gen, 62.65% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 4m 30s. Estimated total time: 15h 12m 38s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 19s. +[2025-08-20 14:18:38,783][__main__][INFO] - Starting iteration 393. +[2025-08-20 14:19:01,670][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:01,672][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:01,678][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:04,133][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:04,134][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:04,141][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:04,143][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:19:04,143][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:19:04,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:05,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:06,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:06,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:07,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:08,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:09,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:09,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:10,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:11,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:12,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:13,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:13,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:14,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:15,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:16,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:17,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:17,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:18,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:19,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:20,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:21,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:22,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:23,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:23,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:24,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:25,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:26,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:27,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:27,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:28,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:29,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:31,156][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:19:32,088][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:19:32,089][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:19:33,458][__main__][INFO] - Iteration 394 took 54s (37.40% Gen, 62.60% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 2m 11s. Estimated total time: 15h 11m 14s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 7s, 500 more iterations: 7h 35m 37s. +[2025-08-20 14:19:33,459][__main__][INFO] - Starting iteration 394. +[2025-08-20 14:19:56,351][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:56,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:56,358][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:58,810][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:58,812][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:58,818][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:19:58,820][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:19:58,821][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:19:59,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:19:59,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:00,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:01,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:02,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:03,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:03,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:04,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:05,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:06,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:07,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:07,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:08,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:09,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:10,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:11,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:11,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:12,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:13,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:14,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:14,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:16,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:17,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:17,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:18,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:19,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:20,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:21,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:21,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:22,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:23,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:24,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:25,884][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:20:26,837][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:20:26,839][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:20:28,469][__main__][INFO] - Iteration 395 took 55s (37.19% Gen, 62.81% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 6m 52s. Estimated total time: 15h 16m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 24s. +[2025-08-20 14:20:28,471][__main__][INFO] - Starting iteration 395. +[2025-08-20 14:20:51,438][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:20:51,439][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:20:51,445][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:20:53,896][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:20:53,897][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:20:53,904][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:20:53,906][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:20:53,907][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:20:54,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:54,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:55,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:56,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:57,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:58,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:58,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:20:59,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:00,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:01,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:02,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:02,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:03,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:04,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:05,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:06,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:06,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:07,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:08,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:09,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:10,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:11,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:12,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:12,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:13,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:14,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:15,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:16,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:16,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:17,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:18,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:19,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:20,897][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:21:21,907][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:21:21,909][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:21:23,274][__main__][INFO] - Iteration 396 took 54s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 2m 30s. Estimated total time: 15h 13m 22s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 41s. +[2025-08-20 14:21:23,275][__main__][INFO] - Starting iteration 396. +[2025-08-20 14:21:46,606][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:21:46,608][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:21:46,614][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:21:49,080][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:21:49,081][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:21:49,088][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:21:49,090][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:21:49,090][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:21:49,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:50,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:50,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:51,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:52,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:53,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:54,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:54,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:55,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:56,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:57,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:58,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:58,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:21:59,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:00,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:01,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:02,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:03,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:04,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:04,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:05,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:06,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:07,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:08,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:08,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:09,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:10,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:11,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:12,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:12,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:13,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:14,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:16,062][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:22:17,036][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:22:17,037][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:22:18,422][__main__][INFO] - Iteration 397 took 55s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 7m 18s. Estimated total time: 15h 19m 6s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 33s. +[2025-08-20 14:22:18,424][__main__][INFO] - Starting iteration 397. +[2025-08-20 14:22:41,502][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:22:41,503][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:22:41,510][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:22:43,961][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:22:43,963][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:22:43,969][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:22:43,971][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:22:43,972][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:22:44,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:45,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:45,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:46,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:47,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:48,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:49,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:49,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:50,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:51,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:52,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:52,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:53,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:54,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:55,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:56,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:56,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:57,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:58,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:22:59,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:00,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:00,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:01,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:02,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:03,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:04,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:05,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:06,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:07,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:07,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:08,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:09,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:11,164][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:23:12,137][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:23:12,138][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:23:13,465][__main__][INFO] - Iteration 398 took 55s (37.50% Gen, 62.50% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 4m 38s. Estimated total time: 15h 17m 20s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 44s, 500 more iterations: 7h 38m 40s. +[2025-08-20 14:23:13,466][__main__][INFO] - Starting iteration 398. +[2025-08-20 14:23:36,618][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:23:36,620][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:23:36,626][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:23:39,083][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:23:39,085][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:23:39,091][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:23:39,093][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:23:39,094][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:23:39,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:40,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:40,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:41,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:42,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:43,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:44,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:44,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:45,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:46,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:47,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:48,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:48,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:49,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:50,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:51,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:52,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:52,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:53,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:54,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:55,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:56,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:56,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:57,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:58,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:23:59,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:00,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:01,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:02,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:02,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:03,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:04,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:06,191][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:24:07,160][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:24:07,162][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:24:08,495][__main__][INFO] - Iteration 399 took 55s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 3m 30s. Estimated total time: 15h 17m 8s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 34s. +[2025-08-20 14:24:08,496][__main__][INFO] - Starting iteration 399. +[2025-08-20 14:24:32,972][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:24:32,974][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:24:32,980][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:24:35,446][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:24:35,447][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:24:35,453][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:24:35,456][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:24:35,456][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:24:35,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:36,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:37,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:38,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:38,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:39,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:40,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:41,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:42,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:42,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:43,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:44,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:45,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:46,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:46,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:47,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:48,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:49,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:50,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:50,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:51,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:52,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:53,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:54,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:55,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:56,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:56,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:57,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:58,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:24:59,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:00,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:00,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:02,515][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:25:03,453][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:25:03,454][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:25:05,205][__main__][INFO] - Iteration 400 took 56s (38.82% Gen, 61.18% Train). Generation: 22s, Training: 34s. Estimated remaining time: 9h 30m 33s. Estimated total time: 15h 45m 7s. Time estimates for 10 more iterations: 9m 27s, 100 more iterations: 1h 34m 30s, 500 more iterations: 7h 52m 33s. +[2025-08-20 14:25:05,206][__main__][INFO] - Starting iteration 400. +[2025-08-20 14:25:28,307][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:25:28,308][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:25:28,315][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:25:30,768][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:25:30,769][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:25:30,776][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:25:30,778][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:25:30,778][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:25:31,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:31,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:32,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:33,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:34,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:35,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:35,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:36,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:37,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:38,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:39,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:39,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:40,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:41,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:42,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:42,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:43,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:45,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:45,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:46,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:47,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:48,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:49,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:49,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:50,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:51,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:52,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:53,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:53,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:54,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:55,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:56,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:25:57,835][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:25:58,806][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:25:58,807][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:26:03,261][__main__][INFO] - Iteration 401 took 58s (35.58% Gen, 58.99% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 52m 2s. Estimated total time: 16h 7m 34s. Time estimates for 10 more iterations: 9m 40s, 100 more iterations: 1h 36m 45s, 500 more iterations: 8h 3m 47s. +[2025-08-20 14:26:03,262][__main__][INFO] - Starting iteration 401. +[2025-08-20 14:26:26,602][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:26:26,604][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:26:26,611][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:26:29,062][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:26:29,063][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:26:29,070][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:26:29,072][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:26:29,073][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:26:29,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:30,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:30,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:31,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:32,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:33,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:34,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:34,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:35,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:36,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:37,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:38,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:38,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:39,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:40,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:41,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:42,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:42,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:43,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:44,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:45,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:46,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:47,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:48,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:48,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:49,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:50,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:51,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:52,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:52,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:53,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:54,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:26:56,106][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:26:57,028][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:26:57,030][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:26:58,433][__main__][INFO] - Iteration 402 took 55s (37.89% Gen, 62.11% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9h 3m 3s. Estimated total time: 15h 19m 30s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 45s. +[2025-08-20 14:26:58,435][__main__][INFO] - Starting iteration 402. +[2025-08-20 14:27:21,373][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:27:21,374][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:27:21,381][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:27:23,858][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:27:23,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:27:23,866][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:27:23,868][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:27:23,869][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:27:24,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:24,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:25,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:26,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:27,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:28,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:28,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:29,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:30,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:31,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:32,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:32,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:33,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:34,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:35,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:36,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:36,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:37,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:38,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:39,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:40,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:40,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:41,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:42,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:43,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:44,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:45,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:46,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:46,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:47,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:48,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:49,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:27:50,918][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:27:51,866][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:27:51,867][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:27:53,233][__main__][INFO] - Iteration 403 took 54s (37.38% Gen, 62.62% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 55m 55s. Estimated total time: 15h 13m 17s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 19s, 500 more iterations: 7h 36m 38s. +[2025-08-20 14:27:53,234][__main__][INFO] - Starting iteration 403. +[2025-08-20 14:28:16,033][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:28:16,034][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:28:16,041][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:28:18,504][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:28:18,506][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:28:18,512][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:28:18,514][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:28:18,515][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:28:18,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:19,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:20,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:21,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:21,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:22,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:23,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:24,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:25,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:25,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:26,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:27,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:28,341][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:36,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:37,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:38,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:39,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:40,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:40,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:41,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:42,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:43,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:44,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:28:45,634][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:28:46,575][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:28:46,576][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:28:48,001][__main__][INFO] - Iteration 404 took 54s (37.16% Gen, 62.84% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 54m 29s. Estimated total time: 15h 12m 46s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 16s, 500 more iterations: 7h 36m 23s. +[2025-08-20 14:28:48,002][__main__][INFO] - Starting iteration 404. +[2025-08-20 14:29:10,966][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:29:10,968][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:29:10,974][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:29:13,431][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:29:13,432][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:29:13,438][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:29:13,440][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:29:13,441][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:29:13,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:14,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:15,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:16,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:16,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:17,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:18,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:19,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:20,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:20,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:21,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:22,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:23,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:24,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:24,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:25,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:26,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:27,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:28,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:29,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:30,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:30,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:31,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:32,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:33,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:34,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:34,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:35,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:36,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:37,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:38,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:38,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:29:40,437][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:29:41,386][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:29:41,387][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:29:42,896][__main__][INFO] - Iteration 405 took 54s (37.34% Gen, 62.66% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 55m 40s. Estimated total time: 15h 14m 52s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 26s. +[2025-08-20 14:29:42,897][__main__][INFO] - Starting iteration 405. +[2025-08-20 14:30:05,765][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:30:05,767][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:30:05,773][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:30:08,225][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:30:08,227][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:30:08,233][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:30:08,235][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:30:08,236][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:30:08,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:09,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:10,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:10,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:11,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:12,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:13,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:14,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:14,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:15,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:16,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:17,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:18,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:18,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:19,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:20,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:21,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:22,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:22,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:24,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:24,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:25,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:26,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:27,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:28,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:28,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:29,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:30,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:31,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:32,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:32,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:33,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:30:35,241][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:30:36,301][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:30:36,303][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:30:37,742][__main__][INFO] - Iteration 406 took 54s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 53m 57s. Estimated total time: 15h 14m 3s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 1s. +[2025-08-20 14:30:37,743][__main__][INFO] - Starting iteration 406. +[2025-08-20 14:31:00,983][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:00,984][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:00,990][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:03,449][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:03,451][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:03,457][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:03,460][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:31:03,460][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:31:03,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:04,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:05,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:06,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:06,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:07,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:08,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:09,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:10,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:10,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:11,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:12,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:13,288][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:21,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:22,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:23,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:24,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:24,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:25,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:26,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:27,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:28,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:28,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:30,575][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:31:31,512][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:31:31,514][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:31:32,838][__main__][INFO] - Iteration 407 took 55s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 57m 12s. Estimated total time: 15h 18m 14s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 7s. +[2025-08-20 14:31:32,840][__main__][INFO] - Starting iteration 407. +[2025-08-20 14:31:55,833][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:55,835][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:55,841][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:58,286][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:58,287][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:58,293][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:31:58,296][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:31:58,296][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:31:58,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:31:59,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:00,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:00,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:01,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:02,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:03,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:04,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:04,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:05,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:06,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:07,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:08,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:08,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:09,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:10,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:11,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:12,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:12,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:13,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:14,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:15,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:16,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:17,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:18,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:18,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:19,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:20,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:21,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:22,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:22,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:23,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:25,254][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:32:26,476][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:32:26,478][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:32:27,931][__main__][INFO] - Iteration 408 took 55s (37.29% Gen, 62.70% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 56m 14s. Estimated total time: 15h 18m 11s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 5s. +[2025-08-20 14:32:27,933][__main__][INFO] - Starting iteration 408. +[2025-08-20 14:32:50,839][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:32:50,841][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:32:50,847][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:32:53,290][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:32:53,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:32:53,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:32:53,300][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:32:53,301][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:32:53,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:54,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:55,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:55,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:56,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:57,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:58,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:59,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:32:59,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:00,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:01,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:02,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:03,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:03,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:04,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:05,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:06,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:07,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:07,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:08,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:09,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:10,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:11,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:11,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:13,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:13,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:14,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:15,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:16,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:17,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:17,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:18,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:20,376][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:33:21,295][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:33:21,297][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:33:22,670][__main__][INFO] - Iteration 409 took 54s (37.40% Gen, 62.60% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 49m 24s. Estimated total time: 15h 12m 16s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 13s, 500 more iterations: 7h 36m 8s. +[2025-08-20 14:33:22,671][__main__][INFO] - Starting iteration 409. +[2025-08-20 14:33:46,657][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:33:46,658][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:33:46,665][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:33:49,120][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:33:49,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:33:49,128][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:33:49,130][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:33:49,131][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:33:49,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:50,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:51,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:51,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:52,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:53,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:54,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:55,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:56,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:56,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:57,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:33:58,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:00,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:01,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:02,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:03,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:04,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:04,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:06,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:07,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:08,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:09,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:10,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:11,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:12,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:12,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:13,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:14,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:15,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:16,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:16,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:17,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:19,592][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:30, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:34:20,522][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:34:20,523][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:34:21,858][__main__][INFO] - Iteration 410 took 59s (36.38% Gen, 63.62% Train). Generation: 21s, Training: 37s. Estimated remaining time: 10h 2m 35s. Estimated total time: 16h 26m 25s. Time estimates for 10 more iterations: 9m 51s, 100 more iterations: 1h 38m 38s, 500 more iterations: 8h 13m 12s. +[2025-08-20 14:34:21,859][__main__][INFO] - Starting iteration 410. +[2025-08-20 14:34:44,888][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:34:44,890][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:34:44,896][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:34:47,368][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:34:47,370][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:34:47,376][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:34:47,378][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:34:47,379][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:34:47,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:48,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:49,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:50,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:50,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:51,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:52,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:53,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:54,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:54,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:55,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:56,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:57,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:58,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:34:59,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:00,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:00,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:01,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:02,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:03,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:04,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:04,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:05,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:06,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:07,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:08,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:08,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:09,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:10,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:11,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:11,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:12,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:14,331][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:35:15,269][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:35:15,270][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:35:16,673][__main__][INFO] - Iteration 411 took 54s (37.52% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 48m 47s. Estimated total time: 15h 13m 33s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 46s. +[2025-08-20 14:35:16,674][__main__][INFO] - Starting iteration 411. +[2025-08-20 14:35:40,841][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:35:40,843][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:35:40,850][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:35:43,322][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:35:43,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:35:43,330][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:35:43,333][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:35:43,333][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:35:43,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:44,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:45,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:46,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:46,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:47,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:48,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:49,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:49,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:50,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:51,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:52,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:53,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:53,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:54,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:55,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:56,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:57,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:57,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:35:59,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:00,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:00,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:01,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:02,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:03,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:04,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:04,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:05,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:06,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:07,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:08,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:08,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:10,400][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:36:11,304][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:36:11,306][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:36:12,610][__main__][INFO] - Iteration 412 took 55s (38.81% Gen, 61.19% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 6m 30s. Estimated total time: 15h 32m 12s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 13s, 500 more iterations: 7h 46m 6s. +[2025-08-20 14:36:12,612][__main__][INFO] - Starting iteration 412. +[2025-08-20 14:36:35,572][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:36:35,574][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:36:35,580][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:36:38,011][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:36:38,012][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:36:38,019][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:36:38,021][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:36:38,022][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:36:38,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:39,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:39,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:40,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:41,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:42,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:43,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:43,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:44,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:45,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:46,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:47,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:47,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:48,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:49,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:50,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:51,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:51,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:52,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:53,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:54,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:55,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:56,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:57,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:57,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:58,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:36:59,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:00,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:01,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:01,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:02,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:03,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:05,033][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:37:05,975][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:37:05,977][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:37:07,719][__main__][INFO] - Iteration 413 took 55s (37.21% Gen, 62.79% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 51m 49s. Estimated total time: 15h 18m 26s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 50s, 500 more iterations: 7h 39m 13s. +[2025-08-20 14:37:07,720][__main__][INFO] - Starting iteration 413. +[2025-08-20 14:37:30,659][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:37:30,660][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:37:30,666][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:37:33,107][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:37:33,108][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:37:33,114][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:37:33,117][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:37:33,117][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:37:33,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:34,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:35,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:35,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:36,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:37,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:38,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:38,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:39,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:40,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:41,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:42,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:42,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:43,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:44,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:45,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:46,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:46,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:47,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:48,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:49,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:50,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:51,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:52,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:52,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:53,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:54,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:55,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:56,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:56,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:57,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:37:58,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:00,088][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:38:01,165][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:38:01,167][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:38:02,568][__main__][INFO] - Iteration 414 took 54s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 46m 35s. Estimated total time: 15h 14m 7s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 3s. +[2025-08-20 14:38:02,570][__main__][INFO] - Starting iteration 414. +[2025-08-20 14:38:26,610][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:38:26,612][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:38:26,618][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:38:29,077][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:38:29,079][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:38:29,085][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:38:29,088][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:38:29,088][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:38:29,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:30,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:30,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:31,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:32,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:33,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:34,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:34,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:35,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:36,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:37,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:38,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:38,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:39,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:40,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:41,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:42,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:42,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:43,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:44,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:45,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:46,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:46,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:47,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:48,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:49,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:50,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:51,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:52,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:52,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:53,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:54,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:38:56,108][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:38:57,137][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:38:57,139][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:38:58,556][__main__][INFO] - Iteration 415 took 55s (38.55% Gen, 61.45% Train). Generation: 21s, Training: 34s. Estimated remaining time: 9h 4m 38s. Estimated total time: 15h 33m 5s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 18s, 500 more iterations: 7h 46m 32s. +[2025-08-20 14:38:58,557][__main__][INFO] - Starting iteration 415. +[2025-08-20 14:39:21,569][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:39:21,570][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:39:21,576][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:39:24,034][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:39:24,035][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:39:24,042][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:39:24,044][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:39:24,045][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:39:24,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:25,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:25,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:26,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:27,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:28,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:29,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:29,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:30,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:31,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:32,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:33,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:33,881][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:42,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:43,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:43,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:44,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:45,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:46,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:47,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:47,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:48,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:49,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:39:51,035][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:39:51,983][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:39:51,985][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:39:53,429][__main__][INFO] - Iteration 416 took 54s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 45m 8s. Estimated total time: 15h 14m 31s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 15s. +[2025-08-20 14:39:53,430][__main__][INFO] - Starting iteration 416. +[2025-08-20 14:40:16,707][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:40:16,708][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:40:16,715][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:40:19,168][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:40:19,170][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:40:19,176][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:40:19,179][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:40:19,179][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:40:19,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:20,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:21,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:21,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:22,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:23,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:24,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:25,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:25,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:26,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:27,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:28,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:29,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:29,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:30,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:31,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:32,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:32,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:33,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:34,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:35,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:36,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:37,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:38,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:39,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:39,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:40,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:41,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:42,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:43,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:43,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:44,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:40:46,179][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:40:47,809][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:40:47,812][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:40:49,373][__main__][INFO] - Iteration 417 took 55s (37.21% Gen, 62.79% Train). Generation: 20s, Training: 35s. Estimated remaining time: 9h 2m 4s. Estimated total time: 15h 32m 22s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 14s, 500 more iterations: 7h 46m 11s. +[2025-08-20 14:40:49,375][__main__][INFO] - Starting iteration 417. +[2025-08-20 14:41:13,136][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:41:13,137][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:41:13,144][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:41:15,619][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:41:15,620][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:41:15,627][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:41:15,629][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:41:15,630][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:41:15,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:16,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:17,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:18,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:19,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:19,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:20,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:21,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:22,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:23,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:23,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:24,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:25,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:26,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:27,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:27,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:28,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:29,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:30,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:31,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:31,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:32,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:33,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:34,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:35,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:36,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:37,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:37,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:38,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:39,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:40,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:41,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:41:42,648][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:41:43,584][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:41:43,586][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:41:45,101][__main__][INFO] - Iteration 418 took 55s (38.18% Gen, 61.82% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 57m 31s. Estimated total time: 15h 28m 45s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 52s, 500 more iterations: 7h 44m 22s. +[2025-08-20 14:41:45,102][__main__][INFO] - Starting iteration 418. +[2025-08-20 14:42:08,029][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:42:08,030][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:42:08,036][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:42:10,486][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:42:10,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:42:10,494][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:42:10,496][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:42:10,497][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:42:10,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:11,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:12,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:13,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:13,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:14,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:15,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:16,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:17,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:17,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:18,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:19,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:20,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:21,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:21,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:22,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:23,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:24,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:25,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:25,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:26,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:27,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:28,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:29,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:29,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:30,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:31,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:32,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:33,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:34,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:35,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:35,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:42:37,435][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:42:38,363][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:42:38,365][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:42:39,747][__main__][INFO] - Iteration 419 took 54s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 38m 34s. Estimated total time: 15h 10m 42s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 4s, 500 more iterations: 7h 35m 21s. +[2025-08-20 14:42:39,749][__main__][INFO] - Starting iteration 419. +[2025-08-20 14:43:02,632][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:02,633][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:02,639][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:05,088][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:05,089][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:05,096][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:05,099][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:43:05,099][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:43:05,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:06,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:06,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:07,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:08,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:09,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:10,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:10,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:11,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:12,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:13,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:14,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:14,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:15,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:16,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:17,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:18,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:18,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:19,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:20,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:21,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:22,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:22,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:24,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:24,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:25,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:26,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:27,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:28,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:28,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:29,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:30,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:43:32,067][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:43:32,999][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:43:33,000][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:43:34,488][__main__][INFO] - Iteration 420 took 54s (37.32% Gen, 62.68% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 39m 15s. Estimated total time: 15h 12m 19s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 13s, 500 more iterations: 7h 36m 9s. +[2025-08-20 14:43:34,490][__main__][INFO] - Starting iteration 420. +[2025-08-20 14:43:57,413][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:57,414][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:57,421][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:59,899][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:59,900][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:59,907][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:43:59,909][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:43:59,910][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:44:00,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:01,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:01,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:02,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:03,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:04,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:04,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:05,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:06,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:07,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:08,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:08,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:09,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:10,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:11,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:12,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:12,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:13,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:14,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:15,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:16,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:16,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:17,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:19,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:19,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:20,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:21,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:22,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:22,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:23,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:24,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:25,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:26,963][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:44:27,883][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:44:27,884][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:44:29,243][__main__][INFO] - Iteration 421 took 54s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 38m 34s. Estimated total time: 15h 12m 32s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 16s. +[2025-08-20 14:44:29,245][__main__][INFO] - Starting iteration 421. +[2025-08-20 14:44:52,542][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:44:52,544][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:44:52,550][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:44:55,008][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:44:55,009][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:44:55,015][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:44:55,018][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:44:55,018][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:44:55,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:56,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:56,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:57,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:58,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:44:59,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:00,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:00,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:01,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:02,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:03,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:04,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:04,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:05,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:06,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:07,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:08,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:09,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:10,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:10,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:11,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:12,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:13,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:14,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:14,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:15,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:16,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:17,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:18,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:18,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:19,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:20,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:22,013][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:45:23,032][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:45:23,034][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:45:24,448][__main__][INFO] - Iteration 422 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 45m 9s. Estimated total time: 15h 20m 2s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 1s. +[2025-08-20 14:45:24,449][__main__][INFO] - Starting iteration 422. +[2025-08-20 14:45:47,439][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:45:47,440][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:45:47,446][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:45:49,913][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:45:49,915][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:45:49,921][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:45:49,923][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:45:49,924][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:45:50,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:51,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:51,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:52,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:53,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:54,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:54,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:55,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:56,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:57,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:58,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:58,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:45:59,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:00,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:01,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:02,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:02,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:03,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:04,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:05,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:06,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:07,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:08,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:09,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:09,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:10,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:11,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:12,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:13,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:13,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:14,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:15,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:16,985][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:46:17,919][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:46:17,920][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:46:19,809][__main__][INFO] - Iteration 423 took 55s (37.06% Gen, 62.94% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 46m 51s. Estimated total time: 15h 22m 39s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 19s. +[2025-08-20 14:46:19,811][__main__][INFO] - Starting iteration 423. +[2025-08-20 14:46:42,849][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:46:42,850][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:46:42,857][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:46:45,322][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:46:45,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:46:45,330][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:46:45,332][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:46:45,333][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:46:45,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:46,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:47,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:48,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:48,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:49,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:50,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:51,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:51,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:52,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:53,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:54,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:55,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:55,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:56,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:57,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:58,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:59,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:46:59,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:00,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:01,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:02,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:03,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:04,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:05,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:06,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:06,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:07,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:08,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:09,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:10,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:10,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:12,470][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:47:13,398][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:47:13,400][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:47:14,725][__main__][INFO] - Iteration 424 took 54s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 38m 29s. Estimated total time: 15h 15m 13s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 36s. +[2025-08-20 14:47:14,726][__main__][INFO] - Starting iteration 424. +[2025-08-20 14:47:37,863][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:47:37,864][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:47:37,871][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:47:40,340][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:47:40,341][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:47:40,347][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:47:40,350][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:47:40,350][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:47:40,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:41,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:42,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:43,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:43,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:44,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:45,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:46,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:46,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:47,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:48,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:49,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:50,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:50,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:51,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:52,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:53,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:54,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:54,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:55,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:56,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:57,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:58,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:47:58,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:00,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:01,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:01,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:02,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:03,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:04,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:05,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:05,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:07,380][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:48:08,287][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:48:08,288][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:48:09,741][__main__][INFO] - Iteration 425 took 55s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 39m 16s. Estimated total time: 15h 16m 54s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 27s. +[2025-08-20 14:48:09,743][__main__][INFO] - Starting iteration 425. +[2025-08-20 14:48:32,681][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:48:32,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:48:32,688][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:48:35,145][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:48:35,147][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:48:35,154][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:48:35,156][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:48:35,156][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:48:35,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:36,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:37,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:37,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:38,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:39,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:40,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:41,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:41,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:42,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:43,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:44,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:44,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:45,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:46,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:47,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:48,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:49,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:50,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:51,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:51,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:52,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:53,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:54,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:54,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:55,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:56,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:57,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:58,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:58,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:48:59,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:00,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:02,099][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:49:03,021][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:49:03,023][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:49:04,494][__main__][INFO] - Iteration 426 took 54s (37.40% Gen, 62.60% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 33m 57s. Estimated total time: 15h 12m 30s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 15s. +[2025-08-20 14:49:04,496][__main__][INFO] - Starting iteration 426. +[2025-08-20 14:49:27,378][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:49:27,379][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:49:27,385][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:49:29,833][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:49:29,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:49:29,841][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:49:29,843][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:49:29,843][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:49:30,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:30,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:31,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:32,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:33,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:34,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:34,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:35,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:36,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:37,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:38,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:38,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:39,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:40,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:41,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:42,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:42,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:43,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:44,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:45,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:46,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:47,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:48,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:48,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:49,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:50,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:51,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:52,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:52,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:53,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:54,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:55,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:49:56,904][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:49:57,864][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:49:57,866][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:49:59,455][__main__][INFO] - Iteration 427 took 54s (37.17% Gen, 62.83% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 36m 30s. Estimated total time: 15h 15m 58s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 59s. +[2025-08-20 14:49:59,457][__main__][INFO] - Starting iteration 427. +[2025-08-20 14:50:22,623][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:50:22,624][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:50:22,630][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:50:25,090][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:50:25,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:50:25,098][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:50:25,100][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:50:25,101][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:50:25,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:26,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:26,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:27,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:28,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:29,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:30,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:30,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:31,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:32,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:33,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:34,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:34,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:35,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:36,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:37,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:38,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:38,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:39,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:40,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:41,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:42,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:42,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:43,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:44,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:45,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:46,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:47,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:48,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:48,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:49,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:50,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:50:52,079][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:50:53,005][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:50:53,006][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:50:54,471][__main__][INFO] - Iteration 428 took 55s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 36m 30s. Estimated total time: 15h 16m 53s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 26s. +[2025-08-20 14:50:54,472][__main__][INFO] - Starting iteration 428. +[2025-08-20 14:51:18,298][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:51:18,299][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:51:18,305][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:51:20,775][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:51:20,776][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:51:20,783][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:51:20,785][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:51:20,785][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:51:21,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:21,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:22,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:23,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:24,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:25,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:25,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:26,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:27,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:28,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:29,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:29,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:30,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:31,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:32,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:33,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:33,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:34,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:35,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:36,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:36,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:37,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:38,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:39,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:40,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:41,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:42,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:42,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:43,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:44,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:45,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:46,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:51:47,712][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:51:48,628][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:51:48,629][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:51:50,324][__main__][INFO] - Iteration 429 took 55s (38.25% Gen, 61.75% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 49m 32s. Estimated total time: 15h 30m 51s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 5s, 500 more iterations: 7h 45m 25s. +[2025-08-20 14:51:50,326][__main__][INFO] - Starting iteration 429. +[2025-08-20 14:52:13,248][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:52:13,249][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:52:13,255][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:52:15,722][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:52:15,723][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:52:15,729][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:52:15,732][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:52:15,732][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:52:16,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:16,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:17,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:18,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:19,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:20,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:20,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:21,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:22,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:23,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:23,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:24,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:25,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:26,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:27,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:27,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:28,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:29,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:30,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:31,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:31,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:32,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:33,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:34,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:35,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:36,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:37,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:38,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:38,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:39,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:40,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:41,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:52:42,948][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:52:43,955][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:52:43,957][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:52:45,286][__main__][INFO] - Iteration 430 took 54s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 33m 45s. Estimated total time: 15h 15m 59s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 59s. +[2025-08-20 14:52:45,287][__main__][INFO] - Starting iteration 430. +[2025-08-20 14:53:08,064][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:53:08,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:53:08,072][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:53:10,519][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:53:10,520][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:53:10,527][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:53:10,529][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:53:10,530][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:53:10,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:11,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:12,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:13,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:14,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:14,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:15,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:16,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:17,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:17,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:18,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:19,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:20,366][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:28,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:29,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:30,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:31,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:31,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:32,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:33,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:34,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:35,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:35,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:53:37,574][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:53:38,508][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:53:38,510][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:53:39,844][__main__][INFO] - Iteration 431 took 54s (37.29% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 26m 8s. Estimated total time: 15h 9m 16s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 55s, 500 more iterations: 7h 34m 38s. +[2025-08-20 14:53:39,846][__main__][INFO] - Starting iteration 431. +[2025-08-20 14:54:02,757][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:02,758][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:02,764][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:05,234][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:05,235][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:05,241][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:05,243][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:54:05,244][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:54:05,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:06,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:07,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:07,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:08,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:09,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:10,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:11,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:11,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:12,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:13,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:14,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:15,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:15,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:16,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:17,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:18,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:19,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:19,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:21,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:21,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:22,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:23,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:24,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:25,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:25,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:26,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:27,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:28,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:29,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:29,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:30,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:54:32,180][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:54:33,183][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:54:33,185][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:54:34,579][__main__][INFO] - Iteration 432 took 54s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 28m 8s. Estimated total time: 15h 12m 12s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 13s, 500 more iterations: 7h 36m 6s. +[2025-08-20 14:54:34,580][__main__][INFO] - Starting iteration 432. +[2025-08-20 14:54:57,807][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:57,808][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:54:57,814][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:00,319][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:00,321][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:00,327][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:00,330][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:55:00,330][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:55:00,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:01,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:02,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:03,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:03,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:04,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:05,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:06,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:06,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:07,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:08,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:09,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:10,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:10,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:11,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:12,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:13,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:14,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:14,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:15,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:16,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:17,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:18,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:18,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:19,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:21,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:21,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:22,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:23,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:24,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:24,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:25,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:27,359][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:55:28,299][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:55:28,301][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:55:29,931][__main__][INFO] - Iteration 433 took 55s (37.49% Gen, 62.51% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 37m 31s. Estimated total time: 15h 22m 30s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 15s. +[2025-08-20 14:55:29,933][__main__][INFO] - Starting iteration 433. +[2025-08-20 14:55:52,952][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:52,953][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:52,960][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:55,398][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:55,399][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:55,406][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:55:55,408][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:55:55,409][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:55:55,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:56,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:57,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:58,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:58,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:55:59,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:00,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:01,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:02,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:02,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:03,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:04,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:05,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:06,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:06,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:07,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:08,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:09,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:10,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:10,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:11,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:12,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:13,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:14,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:15,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:15,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:16,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:17,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:18,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:19,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:19,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:20,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:22,319][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:56:23,268][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:56:23,269][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:56:24,594][__main__][INFO] - Iteration 434 took 54s (37.65% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 25m 7s. Estimated total time: 15h 11m 1s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 6s, 500 more iterations: 7h 35m 30s. +[2025-08-20 14:56:24,596][__main__][INFO] - Starting iteration 434. +[2025-08-20 14:56:47,460][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:56:47,461][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:56:47,468][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:56:49,947][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:56:49,948][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:56:49,955][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:56:49,958][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:56:49,958][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:56:50,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:51,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:51,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:52,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:53,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:54,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:55,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:55,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:56,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:57,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:58,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:58,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:56:59,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:00,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:01,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:02,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:02,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:03,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:04,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:05,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:06,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:07,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:08,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:08,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:09,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:10,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:11,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:12,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:12,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:13,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:14,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:15,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:16,927][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:57:17,865][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:57:17,866][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:57:19,262][__main__][INFO] - Iteration 435 took 54s (37.32% Gen, 62.68% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 24m 17s. Estimated total time: 15h 11m 6s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 6s, 500 more iterations: 7h 35m 33s. +[2025-08-20 14:57:19,264][__main__][INFO] - Starting iteration 435. +[2025-08-20 14:57:42,248][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:57:42,249][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:57:42,256][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:57:44,713][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:57:44,714][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:57:44,721][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:57:44,723][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:57:44,723][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:57:45,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:45,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:46,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:47,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:48,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:48,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:49,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:50,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:51,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:52,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:52,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:53,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:54,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:55,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:56,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:56,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:57,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:59,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:57:59,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:00,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:01,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:02,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:02,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:03,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:04,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:05,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:06,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:06,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:07,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:08,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:09,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:10,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:11,758][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:58:12,725][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:58:12,726][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:58:14,071][__main__][INFO] - Iteration 436 took 54s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 25m 44s. Estimated total time: 15h 13m 27s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 43s. +[2025-08-20 14:58:14,073][__main__][INFO] - Starting iteration 436. +[2025-08-20 14:58:37,096][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:58:37,098][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:58:37,104][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:58:39,558][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:58:39,559][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:58:39,566][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:58:39,568][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:58:39,569][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:58:39,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:40,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:41,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:42,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:43,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:43,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:44,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:45,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:46,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:47,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:47,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:48,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:49,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:50,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:50,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:51,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:52,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:53,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:54,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:54,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:56,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:56,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:57,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:58,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:58:59,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:00,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:00,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:01,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:02,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:03,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:04,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:04,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:06,474][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 14:59:07,457][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 14:59:07,458][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 14:59:08,991][__main__][INFO] - Iteration 437 took 54s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 26m 39s. Estimated total time: 15h 15m 17s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 38s. +[2025-08-20 14:59:08,992][__main__][INFO] - Starting iteration 437. +[2025-08-20 14:59:32,161][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:59:32,162][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:59:32,168][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:59:34,640][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:59:34,642][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:59:34,648][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 14:59:34,650][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 14:59:34,651][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 14:59:34,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:35,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:36,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:37,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:38,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:38,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:39,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:40,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:41,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:42,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:42,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:43,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:44,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:45,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:46,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:46,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:47,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:48,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:49,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:50,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:50,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:51,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:52,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:53,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:54,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:54,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:56,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:56,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:57,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:58,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 14:59:59,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:00,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:01,618][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:00:02,622][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:00:02,624][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:00:04,564][__main__][INFO] - Iteration 438 took 55s (37.26% Gen, 62.74% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 36m 37s. Estimated total time: 15h 26m 11s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 37s, 500 more iterations: 7h 43m 5s. +[2025-08-20 15:00:04,565][__main__][INFO] - Starting iteration 438. +[2025-08-20 15:00:27,633][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:00:27,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:00:27,641][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:00:30,061][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:00:30,063][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:00:30,069][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:00:30,071][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:00:30,072][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:00:30,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:31,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:31,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:32,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:33,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:34,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:35,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:35,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:36,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:37,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:38,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:39,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:39,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:40,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:41,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:42,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:43,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:43,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:44,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:45,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:46,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:47,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:48,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:49,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:49,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:50,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:51,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:52,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:53,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:53,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:54,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:55,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:00:57,052][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:00:58,016][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:00:58,017][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:00:59,364][__main__][INFO] - Iteration 439 took 54s (37.65% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 22m 49s. Estimated total time: 15h 13m 18s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 19s, 500 more iterations: 7h 36m 39s. +[2025-08-20 15:00:59,365][__main__][INFO] - Starting iteration 439. +[2025-08-20 15:01:22,333][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:01:22,334][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:01:22,341][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:01:24,788][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:01:24,789][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:01:24,796][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:01:24,798][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:01:24,799][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:01:25,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:25,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:26,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:27,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:28,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:29,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:29,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:30,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:31,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:32,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:33,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:33,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:34,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:35,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:36,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:37,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:38,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:39,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:39,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:40,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:41,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:42,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:42,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:43,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:44,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:45,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:46,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:46,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:47,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:48,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:49,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:50,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:01:51,720][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:01:52,680][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:01:52,681][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:01:54,085][__main__][INFO] - Iteration 440 took 54s (37.50% Gen, 62.50% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 20m 36s. Estimated total time: 15h 11m 59s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 11s, 500 more iterations: 7h 35m 59s. +[2025-08-20 15:01:54,087][__main__][INFO] - Starting iteration 440. +[2025-08-20 15:02:17,018][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:02:17,019][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:02:17,025][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:02:19,481][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:02:19,483][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:02:19,489][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:02:19,491][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:02:19,492][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:02:19,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:20,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:21,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:22,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:22,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:23,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:24,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:25,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:26,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:26,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:27,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:28,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:29,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:30,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:30,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:31,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:32,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:33,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:34,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:34,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:35,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:36,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:37,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:38,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:39,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:40,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:40,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:41,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:42,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:43,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:44,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:44,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:02:46,513][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:02:47,456][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:02:47,457][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:02:49,032][__main__][INFO] - Iteration 441 took 54s (37.25% Gen, 62.74% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 23m 26s. Estimated total time: 15h 15m 44s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 34s, 500 more iterations: 7h 37m 52s. +[2025-08-20 15:02:49,033][__main__][INFO] - Starting iteration 441. +[2025-08-20 15:03:12,098][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:03:12,099][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:03:12,106][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:03:14,542][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:03:14,543][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:03:14,550][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:03:14,552][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:03:14,553][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:03:14,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:15,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:16,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:17,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:18,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:18,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:19,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:20,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:21,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:22,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:22,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:23,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:24,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:25,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:25,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:26,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:27,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:28,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:29,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:29,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:30,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:31,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:32,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:33,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:34,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:35,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:35,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:36,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:37,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:38,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:39,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:39,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:03:41,522][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:03:42,490][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:03:42,491][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:03:43,828][__main__][INFO] - Iteration 442 took 54s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 20m 1s. Estimated total time: 15h 13m 14s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 19s, 500 more iterations: 7h 36m 37s. +[2025-08-20 15:03:43,830][__main__][INFO] - Starting iteration 442. +[2025-08-20 15:04:07,183][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:04:07,184][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:04:07,190][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:04:09,610][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:04:09,612][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:04:09,618][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:04:09,621][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:04:09,621][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:04:09,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:10,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:11,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:12,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:13,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:13,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:14,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:15,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:16,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:17,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:17,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:18,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:19,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:20,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:21,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:21,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:22,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:23,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:24,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:25,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:25,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:26,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:27,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:28,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:29,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:30,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:31,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:31,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:32,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:33,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:34,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:35,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:04:36,633][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:04:37,599][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:04:37,600][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:04:39,009][__main__][INFO] - Iteration 443 took 55s (37.92% Gen, 62.08% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 25m 30s. Estimated total time: 15h 19m 38s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 49s. +[2025-08-20 15:04:39,015][__main__][INFO] - Starting iteration 443. +[2025-08-20 15:05:02,051][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:02,052][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:02,059][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:04,495][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:04,496][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:04,503][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:04,505][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:05:04,506][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:05:04,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:05,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:06,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:07,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:07,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:08,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:09,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:10,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:11,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:11,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:12,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:13,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:14,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:15,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:15,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:16,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:17,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:18,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:19,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:19,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:20,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:21,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:22,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:23,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:24,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:25,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:25,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:26,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:27,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:28,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:29,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:29,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:05:31,540][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:05:32,482][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:05:32,483][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:05:34,443][__main__][INFO] - Iteration 444 took 55s (37.18% Gen, 62.82% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 28m 39s. Estimated total time: 15h 23m 42s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 22s, 500 more iterations: 7h 41m 51s. +[2025-08-20 15:05:34,445][__main__][INFO] - Starting iteration 444. +[2025-08-20 15:05:57,414][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:57,416][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:57,422][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:59,892][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:59,893][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:59,900][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:05:59,903][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:05:59,903][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:06:00,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:00,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:01,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:02,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:03,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:04,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:04,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:05,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:06,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:07,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:08,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:08,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:09,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:10,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:11,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:12,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:12,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:13,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:14,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:15,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:16,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:16,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:17,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:18,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:19,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:20,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:21,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:22,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:22,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:23,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:24,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:25,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:26,956][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:06:27,915][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:06:27,916][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:06:29,370][__main__][INFO] - Iteration 445 took 54s (37.31% Gen, 62.69% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 19m 26s. Estimated total time: 15h 15m 25s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 42s. +[2025-08-20 15:06:29,372][__main__][INFO] - Starting iteration 445. +[2025-08-20 15:06:52,958][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:06:52,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:06:52,966][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:06:55,446][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:06:55,447][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:06:55,453][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:06:55,456][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:06:55,456][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:06:55,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:56,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:57,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:58,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:58,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:06:59,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:00,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:01,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:02,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:02,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:03,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:04,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:05,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:06,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:06,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:07,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:08,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:09,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:10,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:10,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:11,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:12,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:13,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:14,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:15,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:16,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:16,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:17,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:18,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:19,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:20,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:20,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:22,426][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:07:23,363][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:07:23,364][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:07:24,718][__main__][INFO] - Iteration 446 took 55s (38.14% Gen, 61.86% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 25m 31s. Estimated total time: 15h 22m 25s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 12s. +[2025-08-20 15:07:24,719][__main__][INFO] - Starting iteration 446. +[2025-08-20 15:07:47,631][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:07:47,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:07:47,639][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:07:50,104][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:07:50,105][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:07:50,112][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:07:50,114][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:07:50,115][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:07:50,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:51,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:51,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:52,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:53,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:54,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:55,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:55,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:56,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:57,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:58,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:59,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:07:59,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:00,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:01,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:02,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:03,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:04,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:05,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:05,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:06,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:07,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:08,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:09,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:09,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:10,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:11,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:12,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:13,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:13,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:14,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:15,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:17,072][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:08:18,027][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:08:18,029][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:08:19,644][__main__][INFO] - Iteration 447 took 54s (37.23% Gen, 62.77% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 17m 36s. Estimated total time: 15h 15m 24s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 42s. +[2025-08-20 15:08:19,646][__main__][INFO] - Starting iteration 447. +[2025-08-20 15:08:42,900][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:08:42,901][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:08:42,908][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:08:45,345][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:08:45,346][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:08:45,353][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:08:45,355][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:08:45,356][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:08:45,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:46,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:47,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:48,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:48,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:49,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:50,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:51,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:52,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:52,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:53,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:54,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:55,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:55,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:56,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:57,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:58,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:08:59,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:00,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:01,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:01,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:02,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:03,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:04,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:05,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:05,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:06,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:07,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:08,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:09,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:09,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:10,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:12,322][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:09:13,333][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:09:13,336][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:09:14,716][__main__][INFO] - Iteration 448 took 55s (37.78% Gen, 62.22% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 19m 5s. Estimated total time: 15h 17m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 54s. +[2025-08-20 15:09:14,717][__main__][INFO] - Starting iteration 448. +[2025-08-20 15:09:37,741][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:09:37,742][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:09:37,748][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:09:40,207][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:09:40,209][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:09:40,215][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:09:40,218][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:09:40,218][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:09:40,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:41,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:42,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:42,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:43,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:44,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:45,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:46,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:46,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:47,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:48,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:49,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:50,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:50,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:51,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:52,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:53,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:54,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:54,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:55,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:56,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:57,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:58,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:09:59,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:00,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:00,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:01,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:02,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:03,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:03,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:04,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:05,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:07,133][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:10:08,039][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:10:08,041][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:10:11,584][__main__][INFO] - Iteration 449 took 56s (36.19% Gen, 63.81% Train). Generation: 20s, Training: 36s. Estimated remaining time: 8h 48m 5s. Estimated total time: 15h 47m 46s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 46s, 500 more iterations: 7h 53m 53s. +[2025-08-20 15:10:11,585][__main__][INFO] - Starting iteration 449. +[2025-08-20 15:10:34,578][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:10:34,579][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:10:34,585][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:10:37,067][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:10:37,068][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:10:37,075][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:10:37,077][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:10:37,078][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:10:37,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:38,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:38,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:39,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:40,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:41,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:42,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:42,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:43,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:44,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:45,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:46,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:46,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:47,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:48,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:49,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:50,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:50,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:51,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:52,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:53,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:54,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:54,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:56,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:56,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:57,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:58,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:10:59,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:00,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:00,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:01,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:02,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:04,043][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:11:04,972][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:11:04,973][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:11:06,328][__main__][INFO] - Iteration 450 took 54s (37.50% Gen, 62.50% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 11m 46s. Estimated total time: 15h 12m 22s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 14s, 500 more iterations: 7h 36m 11s. +[2025-08-20 15:11:06,329][__main__][INFO] - Starting iteration 450. +[2025-08-20 15:11:29,277][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:11:29,278][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:11:29,284][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:11:31,743][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:11:31,745][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:11:31,752][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:11:31,754][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:11:31,754][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:11:32,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:32,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:33,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:34,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:35,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:36,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:36,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:37,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:38,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:39,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:39,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:40,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:41,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:42,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:43,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:43,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:44,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:45,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:46,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:47,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:47,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:48,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:49,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:50,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:51,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:52,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:53,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:53,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:54,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:55,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:56,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:57,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:11:58,670][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:11:59,607][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:11:59,608][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:12:03,874][__main__][INFO] - Iteration 451 took 57s (35.56% Gen, 59.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 57m 31s. Estimated total time: 15h 59m 3s. Time estimates for 10 more iterations: 9m 35s, 100 more iterations: 1h 35m 54s, 500 more iterations: 7h 59m 31s. +[2025-08-20 15:12:03,875][__main__][INFO] - Starting iteration 451. +[2025-08-20 15:12:27,356][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:12:27,358][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:12:27,364][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:12:29,813][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:12:29,814][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:12:29,821][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:12:29,823][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:12:29,824][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:12:30,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:30,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:31,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:32,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:33,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:34,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:34,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:35,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:36,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:37,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:38,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:38,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:39,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:40,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:41,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:42,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:42,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:43,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:44,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:45,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:46,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:47,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:48,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:48,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:49,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:50,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:51,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:52,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:52,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:53,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:54,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:55,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:12:56,779][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:12:57,697][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:12:57,698][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:12:59,386][__main__][INFO] - Iteration 452 took 55s (37.88% Gen, 62.12% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 22m 41s. Estimated total time: 15h 25m 9s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 30s, 500 more iterations: 7h 42m 34s. +[2025-08-20 15:12:59,387][__main__][INFO] - Starting iteration 452. +[2025-08-20 15:13:23,621][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:13:23,622][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:13:23,628][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:13:26,072][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:13:26,073][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:13:26,079][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:13:26,082][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:13:26,082][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:13:26,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:27,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:27,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:28,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:29,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:30,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:31,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:31,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:32,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:33,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:34,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:35,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:35,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:36,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:37,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:38,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:39,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:39,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:40,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:41,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:42,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:43,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:44,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:45,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:45,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:46,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:47,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:48,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:49,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:49,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:50,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:51,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:13:53,109][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:13:54,299][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:13:54,301][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:13:55,773][__main__][INFO] - Iteration 453 took 56s (38.67% Gen, 61.33% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 36m 20s. Estimated total time: 15h 39m 45s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 58s, 500 more iterations: 7h 49m 52s. +[2025-08-20 15:13:55,774][__main__][INFO] - Starting iteration 453. +[2025-08-20 15:14:20,395][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:14:20,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:14:20,402][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:14:22,856][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:14:22,857][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:14:22,863][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:14:22,866][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:14:22,866][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:14:23,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:23,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:24,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:25,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:26,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:27,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:27,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:28,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:29,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:30,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:31,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:31,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:32,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:33,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:34,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:35,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:35,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:36,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:37,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:38,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:39,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:40,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:41,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:41,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:42,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:43,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:44,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:45,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:45,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:46,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:47,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:48,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:14:49,768][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:14:50,680][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:14:50,681][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:14:52,035][__main__][INFO] - Iteration 454 took 56s (39.39% Gen, 60.61% Train). Generation: 22s, Training: 34s. Estimated remaining time: 8h 33m 19s. Estimated total time: 15h 37m 40s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 46s, 500 more iterations: 7h 48m 50s. +[2025-08-20 15:14:52,036][__main__][INFO] - Starting iteration 454. +[2025-08-20 15:15:15,100][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:15:15,102][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:15:15,108][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:15:17,561][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:15:17,563][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:15:17,569][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:15:17,571][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:15:17,572][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:15:17,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:18,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:19,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:20,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:21,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:21,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:22,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:23,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:24,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:25,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:25,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:26,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:27,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:28,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:28,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:29,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:30,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:31,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:32,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:32,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:33,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:34,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:35,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:36,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:37,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:38,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:38,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:39,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:40,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:41,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:42,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:42,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:15:44,439][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:15:45,510][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:15:45,513][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:15:46,829][__main__][INFO] - Iteration 455 took 54s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 7m 56s. Estimated total time: 15h 13m 12s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 19s, 500 more iterations: 7h 36m 36s. +[2025-08-20 15:15:46,830][__main__][INFO] - Starting iteration 455. +[2025-08-20 15:16:09,971][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:16:09,972][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:16:09,978][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:16:12,458][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:16:12,459][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:16:12,466][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:16:12,468][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:16:12,469][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:16:12,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:13,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:14,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:15,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:15,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:16,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:17,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:18,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:19,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:19,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:20,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:21,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:22,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:23,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:23,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:24,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:25,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:26,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:27,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:27,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:29,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:29,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:30,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:31,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:32,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:33,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:33,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:34,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:35,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:36,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:37,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:37,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:16:39,475][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:16:40,446][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:16:40,447][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:16:41,792][__main__][INFO] - Iteration 456 took 54s (37.57% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 9m 50s. Estimated total time: 15h 16m 0s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 0s. +[2025-08-20 15:16:41,793][__main__][INFO] - Starting iteration 456. +[2025-08-20 15:17:05,248][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:17:05,250][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:17:05,256][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:17:07,701][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:17:07,702][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:17:07,708][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:17:07,711][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:17:07,711][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:17:08,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:08,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:09,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:10,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:11,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:11,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:12,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:13,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:14,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:15,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:15,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:16,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:17,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:18,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:19,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:19,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:20,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:21,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:22,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:23,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:24,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:25,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:25,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:26,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:27,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:28,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:29,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:29,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:30,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:31,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:32,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:33,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:17:34,734][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:17:35,661][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:17:35,662][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:17:36,964][__main__][INFO] - Iteration 457 took 55s (38.09% Gen, 61.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 12m 24s. Estimated total time: 15h 19m 30s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 45s. +[2025-08-20 15:17:36,966][__main__][INFO] - Starting iteration 457. +[2025-08-20 15:18:00,388][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:00,390][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:00,396][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:02,843][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:02,844][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:02,850][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:02,853][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:18:02,854][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:18:03,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:03,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:04,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:05,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:06,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:07,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:07,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:08,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:09,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:10,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:11,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:11,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:12,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:13,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:14,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:15,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:15,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:16,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:17,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:18,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:19,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:20,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:21,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:21,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:22,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:23,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:24,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:25,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:25,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:26,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:27,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:28,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:29,793][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:18:30,760][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:18:30,761][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:18:32,310][__main__][INFO] - Iteration 458 took 55s (37.85% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 14m 23s. Estimated total time: 15h 22m 24s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 12s. +[2025-08-20 15:18:32,312][__main__][INFO] - Starting iteration 458. +[2025-08-20 15:18:55,297][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:55,298][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:55,305][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:57,769][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:57,771][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:57,777][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:18:57,780][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:18:57,780][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:18:58,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:58,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:18:59,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:00,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:01,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:02,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:02,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:03,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:04,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:05,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:06,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:06,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:07,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:08,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:09,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:09,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:10,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:11,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:12,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:13,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:14,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:15,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:16,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:16,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:17,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:18,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:19,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:20,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:20,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:21,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:22,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:23,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:24,807][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:19:25,762][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:19:25,764][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:19:27,015][__main__][INFO] - Iteration 459 took 54s (37.52% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 2m 46s. Estimated total time: 15h 11m 42s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 10s, 500 more iterations: 7h 35m 51s. +[2025-08-20 15:19:27,016][__main__][INFO] - Starting iteration 459. +[2025-08-20 15:19:50,193][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:19:50,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:19:50,201][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:19:52,666][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:19:52,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:19:52,674][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:19:52,676][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:19:52,677][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:19:52,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:53,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:54,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:55,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:56,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:56,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:57,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:58,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:19:59,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:00,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:00,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:01,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:02,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:03,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:04,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:04,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:05,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:06,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:07,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:08,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:08,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:09,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:10,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:11,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:12,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:13,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:14,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:14,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:15,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:16,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:17,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:18,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:19,623][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:20:20,601][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:20:20,602][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:20:22,031][__main__][INFO] - Iteration 460 took 55s (37.64% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 7m 3s. Estimated total time: 15h 16m 54s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 27s. +[2025-08-20 15:20:22,033][__main__][INFO] - Starting iteration 460. +[2025-08-20 15:20:45,027][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:20:45,028][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:20:45,035][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:20:47,490][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:20:47,491][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:20:47,498][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:20:47,500][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:20:47,501][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:20:47,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:48,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:49,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:50,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:50,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:51,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:52,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:53,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:54,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:54,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:55,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:56,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:57,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:58,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:58,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:20:59,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:00,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:01,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:02,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:02,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:03,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:04,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:05,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:06,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:07,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:08,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:08,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:09,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:10,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:11,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:12,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:12,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:14,500][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:21:15,420][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:21:15,421][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:21:16,859][__main__][INFO] - Iteration 461 took 54s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 2m 59s. Estimated total time: 15h 13m 45s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 52s. +[2025-08-20 15:21:16,860][__main__][INFO] - Starting iteration 461. +[2025-08-20 15:21:39,835][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:21:39,837][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:21:39,843][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:21:42,327][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:21:42,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:21:42,335][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:21:42,337][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:21:42,338][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:21:42,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:43,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:44,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:45,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:45,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:46,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:47,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:48,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:48,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:49,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:50,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:51,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:52,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:52,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:53,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:55,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:55,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:56,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:57,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:58,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:59,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:21:59,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:00,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:01,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:02,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:02,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:03,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:04,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:05,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:06,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:06,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:07,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:09,367][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:22:10,320][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:22:10,322][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:22:11,715][__main__][INFO] - Iteration 462 took 54s (37.42% Gen, 62.57% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 2m 33s. Estimated total time: 15h 14m 14s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 25s, 500 more iterations: 7h 37m 7s. +[2025-08-20 15:22:11,716][__main__][INFO] - Starting iteration 462. +[2025-08-20 15:22:34,901][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:22:34,903][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:22:34,909][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:22:37,383][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:22:37,384][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:22:37,391][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:22:37,394][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:22:37,394][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:22:37,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:38,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:39,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:40,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:40,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:41,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:42,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:43,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:44,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:44,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:45,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:46,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:47,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:48,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:48,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:49,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:50,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:51,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:51,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:52,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:53,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:54,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:55,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:56,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:57,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:57,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:58,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:22:59,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:00,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:01,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:01,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:02,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:04,283][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:23:05,226][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:23:05,227][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:23:06,618][__main__][INFO] - Iteration 463 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 2m 25s. Estimated total time: 15h 15m 1s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 30s. +[2025-08-20 15:23:06,620][__main__][INFO] - Starting iteration 463. +[2025-08-20 15:23:29,986][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:23:29,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:23:29,994][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:23:32,462][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:23:32,463][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:23:32,469][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:23:32,472][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:23:32,473][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:23:32,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:33,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:34,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:35,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:35,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:36,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:37,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:38,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:39,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:39,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:40,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:41,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:42,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:43,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:43,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:44,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:45,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:46,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:47,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:47,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:49,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:49,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:50,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:51,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:52,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:53,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:53,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:54,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:55,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:56,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:57,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:57,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:23:59,438][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:24:00,409][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:24:00,411][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:24:01,700][__main__][INFO] - Iteration 464 took 55s (37.97% Gen, 62.03% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 4m 28s. Estimated total time: 15h 17m 59s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 59s. +[2025-08-20 15:24:01,701][__main__][INFO] - Starting iteration 464. +[2025-08-20 15:24:24,767][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:24:24,769][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:24:24,775][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:24:27,223][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:24:27,224][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:24:27,230][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:24:27,233][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:24:27,233][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:24:27,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:28,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:29,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:29,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:30,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:31,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:32,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:33,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:33,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:34,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:35,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:36,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:37,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:37,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:38,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:39,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:40,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:41,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:41,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:42,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:43,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:44,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:45,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:46,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:47,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:47,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:48,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:49,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:50,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:51,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:51,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:52,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:24:54,259][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:24:55,189][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:24:55,190][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:24:56,736][__main__][INFO] - Iteration 465 took 55s (37.49% Gen, 62.51% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 2m 49s. Estimated total time: 15h 17m 14s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 37s. +[2025-08-20 15:24:56,738][__main__][INFO] - Starting iteration 465. +[2025-08-20 15:25:19,776][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:25:19,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:25:19,784][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:25:22,264][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:25:22,266][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:25:22,272][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:25:22,275][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:25:22,275][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:25:22,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:23,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:24,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:24,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:25,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:26,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:27,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:28,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:28,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:29,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:30,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:31,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:32,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:32,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:33,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:34,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:35,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:36,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:37,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:38,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:38,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:39,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:40,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:41,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:42,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:42,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:43,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:44,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:45,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:46,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:46,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:47,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:25:49,165][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:25:50,107][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:25:50,109][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:25:51,550][__main__][INFO] - Iteration 466 took 54s (37.55% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 58m 10s. Estimated total time: 15h 13m 31s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 45s. +[2025-08-20 15:25:51,551][__main__][INFO] - Starting iteration 466. +[2025-08-20 15:26:14,615][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:26:14,617][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:26:14,623][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:26:17,114][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:26:17,116][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:26:17,122][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:26:17,124][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:26:17,125][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:26:17,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:18,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:19,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:19,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:20,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:21,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:22,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:22,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:23,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:24,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:25,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:26,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:26,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:27,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:28,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:29,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:30,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:31,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:32,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:32,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:33,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:34,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:35,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:36,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:36,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:37,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:38,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:39,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:40,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:40,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:41,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:42,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:26:44,053][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:26:45,012][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:26:45,014][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:26:46,401][__main__][INFO] - Iteration 467 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 57m 53s. Estimated total time: 15h 14m 9s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 4s. +[2025-08-20 15:26:46,402][__main__][INFO] - Starting iteration 467. +[2025-08-20 15:27:10,272][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:27:10,273][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:27:10,280][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:27:12,786][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:27:12,787][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:27:12,794][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:27:12,796][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:27:12,796][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:27:13,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:13,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:14,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:15,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:16,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:17,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:17,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:18,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:19,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:20,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:21,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:21,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:22,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:23,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:24,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:25,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:25,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:26,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:27,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:28,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:28,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:29,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:30,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:31,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:32,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:32,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:33,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:35,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:35,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:36,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:37,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:38,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:27:39,788][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:27:40,705][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:27:40,707][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:27:42,079][__main__][INFO] - Iteration 468 took 55s (38.40% Gen, 61.60% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 10m 45s. Estimated total time: 15h 27m 56s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 47s, 500 more iterations: 7h 43m 58s. +[2025-08-20 15:27:42,081][__main__][INFO] - Starting iteration 468. +[2025-08-20 15:28:05,586][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:28:05,587][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:28:05,593][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:28:08,068][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:28:08,069][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:28:08,075][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:28:08,078][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:28:08,078][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:28:08,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:09,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:09,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:10,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:11,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:12,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:13,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:13,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:14,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:15,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:16,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:17,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:17,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:18,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:19,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:20,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:21,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:21,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:22,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:23,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:24,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:25,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:25,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:27,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:27,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:28,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:29,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:30,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:31,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:31,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:32,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:33,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:28:35,086][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:28:36,066][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:28:36,068][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:28:37,417][__main__][INFO] - Iteration 469 took 55s (38.02% Gen, 61.98% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 4m 9s. Estimated total time: 15h 22m 16s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 8s. +[2025-08-20 15:28:37,421][__main__][INFO] - Starting iteration 469. +[2025-08-20 15:29:00,510][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:29:00,512][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:29:00,518][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:29:02,993][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:29:02,995][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:29:03,001][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:29:03,003][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:29:03,004][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:29:03,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:04,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:04,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:05,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:06,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:07,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:08,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:08,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:09,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:10,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:11,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:12,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:12,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:13,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:14,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:15,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:16,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:16,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:17,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:18,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:19,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:19,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:20,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:22,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:22,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:23,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:24,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:25,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:26,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:26,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:27,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:28,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:29:30,025][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:29:30,980][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:29:30,982][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:29:32,572][__main__][INFO] - Iteration 470 took 55s (37.39% Gen, 62.61% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 0m 8s. Estimated total time: 15h 19m 9s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 34s. +[2025-08-20 15:29:32,573][__main__][INFO] - Starting iteration 470. +[2025-08-20 15:30:00,282][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:00,284][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:00,290][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:02,749][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:02,750][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:02,757][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:02,759][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:30:02,759][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:30:03,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:03,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:04,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:05,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:06,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:07,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:07,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:08,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:09,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:10,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:10,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:11,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:12,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:13,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:14,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:14,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:15,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:16,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:17,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:18,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:19,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:20,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:20,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:21,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:22,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:23,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:24,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:24,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:25,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:26,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:27,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:28,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:29,767][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:30:30,746][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:30:30,747][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:30:32,248][__main__][INFO] - Iteration 471 took 59s (42.30% Gen, 57.70% Train). Generation: 25s, Training: 34s. Estimated remaining time: 9h 14m 32s. Estimated total time: 16h 34m 33s. Time estimates for 10 more iterations: 9m 56s, 100 more iterations: 1h 39m 27s, 500 more iterations: 8h 17m 16s. +[2025-08-20 15:30:32,249][__main__][INFO] - Starting iteration 471. +[2025-08-20 15:30:56,383][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:56,384][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:56,390][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:58,854][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:58,855][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:58,861][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:30:58,864][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:30:58,864][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:30:59,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:30:59,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:00,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:01,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:02,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:03,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:03,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:04,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:05,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:06,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:07,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:07,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:08,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:09,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:10,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:11,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:11,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:12,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:13,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:14,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:15,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:16,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:17,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:17,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:18,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:19,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:20,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:20,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:21,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:22,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:23,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:24,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:25,773][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:31:26,693][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:31:26,694][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:31:28,055][__main__][INFO] - Iteration 472 took 55s (38.84% Gen, 61.16% Train). Generation: 21s, Training: 34s. Estimated remaining time: 8h 9m 8s. Estimated total time: 15h 30m 5s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 0s, 500 more iterations: 7h 45m 2s. +[2025-08-20 15:31:28,057][__main__][INFO] - Starting iteration 472. +[2025-08-20 15:31:51,424][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:31:51,426][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:31:51,432][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:31:53,892][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:31:53,894][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:31:53,900][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:31:53,902][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:31:53,903][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:31:54,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:54,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:55,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:56,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:57,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:58,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:58,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:31:59,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:00,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:01,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:02,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:02,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:03,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:04,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:05,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:06,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:06,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:07,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:08,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:09,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:10,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:11,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:12,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:13,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:13,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:14,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:15,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:16,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:17,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:17,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:18,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:19,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:21,003][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:32:21,921][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:32:21,923][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:32:25,194][__main__][INFO] - Iteration 473 took 57s (36.62% Gen, 63.38% Train). Generation: 20s, Training: 36s. Estimated remaining time: 8h 30m 22s. Estimated total time: 15h 52m 16s. Time estimates for 10 more iterations: 9m 31s, 100 more iterations: 1h 35m 13s, 500 more iterations: 7h 56m 8s. +[2025-08-20 15:32:25,195][__main__][INFO] - Starting iteration 473. +[2025-08-20 15:32:48,288][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:32:48,289][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:32:48,295][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:32:50,741][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:32:50,742][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:32:50,749][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:32:50,751][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:32:50,752][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:32:51,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:51,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:52,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:53,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:54,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:55,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:55,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:56,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:57,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:58,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:58,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:32:59,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:00,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:01,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:02,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:02,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:03,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:04,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:05,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:06,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:07,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:08,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:08,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:09,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:10,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:12,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:13,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:13,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:14,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:15,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:16,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:17,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:18,657][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:33:23,230][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:33:23,231][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:33:24,662][__main__][INFO] - Iteration 474 took 59s (34.68% Gen, 65.32% Train). Generation: 20s, Training: 38s. Estimated remaining time: 9h 8m 12s. Estimated total time: 16h 31m 6s. Time estimates for 10 more iterations: 9m 54s, 100 more iterations: 1h 39m 6s, 500 more iterations: 8h 15m 33s. +[2025-08-20 15:33:24,732][__main__][INFO] - Starting iteration 474. +[2025-08-20 15:33:47,721][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:33:47,722][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:33:47,729][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:33:50,179][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:33:50,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:33:50,187][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:33:50,189][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:33:50,189][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:33:50,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:51,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:52,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:52,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:53,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:54,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:55,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:56,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:56,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:57,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:58,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:33:59,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:00,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:00,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:01,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:02,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:03,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:03,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:04,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:05,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:06,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:07,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:07,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:08,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:09,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:10,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:11,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:12,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:13,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:13,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:14,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:15,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:17,111][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:34:18,057][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:34:18,058][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:34:19,463][__main__][INFO] - Iteration 475 took 54s (37.54% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 48m 21s. Estimated total time: 15h 12m 9s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 12s, 500 more iterations: 7h 36m 4s. +[2025-08-20 15:34:19,464][__main__][INFO] - Starting iteration 475. +[2025-08-20 15:34:42,908][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:34:42,910][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:34:42,916][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:34:45,351][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:34:45,352][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:34:45,359][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:34:45,361][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:34:45,362][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:34:45,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:46,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:47,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:48,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:48,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:49,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:50,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:51,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:52,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:52,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:53,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:54,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:55,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:55,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:56,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:57,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:58,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:59,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:34:59,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:01,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:02,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:02,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:03,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:04,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:05,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:06,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:06,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:07,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:08,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:09,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:09,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:10,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:12,361][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:35:13,309][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:35:13,311][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:35:14,795][__main__][INFO] - Iteration 476 took 55s (37.99% Gen, 62.01% Train). Generation: 21s, Training: 34s. Estimated remaining time: 7h 57m 26s. Estimated total time: 15h 22m 10s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 5s. +[2025-08-20 15:35:14,796][__main__][INFO] - Starting iteration 476. +[2025-08-20 15:35:37,872][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:35:37,873][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:35:37,880][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:35:40,335][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:35:40,336][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:35:40,342][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:35:40,345][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:35:40,345][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:35:40,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:41,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:42,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:43,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:43,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:44,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:45,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:46,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:47,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:47,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:48,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:49,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:50,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:50,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:51,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:52,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:53,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:54,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:54,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:55,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:56,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:57,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:58,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:35:59,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:00,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:00,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:01,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:02,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:03,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:04,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:04,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:05,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:07,307][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:36:08,303][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:36:08,540][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:36:09,891][__main__][INFO] - Iteration 477 took 55s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 52m 35s. Estimated total time: 15h 18m 13s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 6s. +[2025-08-20 15:36:09,892][__main__][INFO] - Starting iteration 477. +[2025-08-20 15:36:33,282][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:36:33,284][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:36:33,290][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:36:35,791][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:36:35,793][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:36:35,799][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:36:35,801][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:36:35,802][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:36:36,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:36,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:37,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:38,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:39,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:40,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:40,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:41,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:42,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:43,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:44,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:44,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:45,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:46,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:47,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:47,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:48,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:50,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:50,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:51,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:52,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:53,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:54,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:54,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:55,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:56,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:57,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:57,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:58,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:36:59,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:00,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:01,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:02,769][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:37:03,716][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:37:03,718][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:37:05,036][__main__][INFO] - Iteration 478 took 55s (37.91% Gen, 62.09% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 52m 30s. Estimated total time: 15h 19m 4s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 32s. +[2025-08-20 15:37:05,038][__main__][INFO] - Starting iteration 478. +[2025-08-20 15:37:28,148][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:37:28,149][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:37:28,155][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:37:30,586][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:37:30,587][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:37:30,593][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:37:30,595][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:37:30,596][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:37:30,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:31,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:32,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:33,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:34,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:34,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:35,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:36,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:37,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:38,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:38,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:39,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:40,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:41,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:42,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:42,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:43,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:44,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:45,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:46,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:47,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:48,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:48,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:49,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:50,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:51,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:52,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:52,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:53,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:54,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:55,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:56,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:37:57,627][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:37:58,612][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:37:58,614][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:38:00,050][__main__][INFO] - Iteration 479 took 55s (37.59% Gen, 62.40% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 49m 22s. Estimated total time: 15h 16m 51s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 25s. +[2025-08-20 15:38:00,051][__main__][INFO] - Starting iteration 479. +[2025-08-20 15:38:23,323][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:38:23,325][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:38:23,331][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:38:25,778][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:38:25,779][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:38:25,786][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:38:25,788][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:38:25,788][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:38:26,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:26,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:27,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:28,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:29,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:30,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:30,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:31,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:32,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:33,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:34,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:34,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:35,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:36,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:37,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:37,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:38,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:39,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:40,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:41,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:41,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:42,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:44,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:44,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:45,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:46,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:47,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:48,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:48,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:49,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:50,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:51,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:38:52,820][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:38:53,754][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:38:53,765][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:38:55,134][__main__][INFO] - Iteration 480 took 55s (37.80% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 49m 38s. Estimated total time: 15h 18m 2s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 1s. +[2025-08-20 15:38:55,135][__main__][INFO] - Starting iteration 480. +[2025-08-20 15:39:18,681][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:39:18,683][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:39:18,689][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:39:21,142][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:39:21,143][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:39:21,149][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:39:21,152][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:39:21,152][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:39:21,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:22,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:23,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:23,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:24,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:25,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:26,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:27,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:27,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:28,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:29,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:30,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:30,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:31,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:32,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:33,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:34,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:34,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:35,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:36,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:37,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:38,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:38,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:39,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:40,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:41,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:42,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:42,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:44,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:44,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:45,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:46,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:39:48,144][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:39:49,365][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:39:49,367][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:39:50,684][__main__][INFO] - Iteration 481 took 55s (37.99% Gen, 62.01% Train). Generation: 21s, Training: 34s. Estimated remaining time: 7h 56m 28s. Estimated total time: 15h 25m 48s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 34s, 500 more iterations: 7h 42m 54s. +[2025-08-20 15:39:50,685][__main__][INFO] - Starting iteration 481. +[2025-08-20 15:40:13,672][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:40:13,673][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:40:13,680][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:40:16,151][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:40:16,152][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:40:16,159][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:40:16,160][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:40:16,161][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:40:16,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:17,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:18,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:18,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:19,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:20,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:21,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:22,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:22,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:23,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:24,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:25,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:25,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:26,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:27,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:28,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:29,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:29,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:30,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:32,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:32,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:33,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:34,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:35,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:36,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:36,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:37,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:38,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:39,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:39,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:40,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:41,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:40:43,195][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:40:44,118][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:40:44,120][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:40:45,420][__main__][INFO] - Iteration 482 took 54s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 41m 59s. Estimated total time: 15h 12m 14s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 13s, 500 more iterations: 7h 36m 7s. +[2025-08-20 15:40:45,421][__main__][INFO] - Starting iteration 482. +[2025-08-20 15:41:08,487][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:41:08,489][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:41:08,495][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:41:10,943][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:41:10,945][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:41:10,951][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:41:10,953][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:41:10,954][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:41:11,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:12,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:12,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:13,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:14,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:15,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:16,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:16,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:17,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:18,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:19,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:19,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:20,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:21,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:22,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:23,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:23,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:24,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:25,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:26,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:27,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:27,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:29,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:29,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:30,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:31,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:32,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:33,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:33,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:34,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:35,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:36,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:41:37,874][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:41:38,788][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:41:38,790][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:41:40,125][__main__][INFO] - Iteration 483 took 54s (37.71% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 40m 34s. Estimated total time: 15h 11m 43s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 10s, 500 more iterations: 7h 35m 51s. +[2025-08-20 15:41:40,127][__main__][INFO] - Starting iteration 483. +[2025-08-20 15:42:03,489][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:03,491][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:03,497][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:05,939][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:05,940][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:05,947][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:05,949][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:42:05,949][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:42:06,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:07,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:07,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:08,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:09,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:10,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:11,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:11,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:12,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:13,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:14,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:14,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:15,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:16,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:17,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:18,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:18,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:19,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:21,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:21,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:22,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:23,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:24,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:24,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:25,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:26,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:27,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:28,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:28,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:29,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:30,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:31,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:42:32,953][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:42:33,893][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:42:33,895][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:42:35,253][__main__][INFO] - Iteration 484 took 55s (37.96% Gen, 62.04% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 46m 42s. Estimated total time: 15h 18m 46s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 23s. +[2025-08-20 15:42:35,255][__main__][INFO] - Starting iteration 484. +[2025-08-20 15:42:58,247][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:58,249][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:42:58,255][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:00,705][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:00,706][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:00,712][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:00,715][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:43:00,715][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:43:01,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:01,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:02,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:03,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:04,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:04,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:05,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:06,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:07,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:08,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:08,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:09,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:10,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:11,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:12,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:12,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:13,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:14,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:15,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:16,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:16,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:17,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:18,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:19,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:20,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:21,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:22,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:22,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:23,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:24,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:25,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:26,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:27,671][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:43:28,619][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:43:28,620][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:43:29,957][__main__][INFO] - Iteration 485 took 54s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 38m 43s. Estimated total time: 15h 11m 42s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 10s, 500 more iterations: 7h 35m 51s. +[2025-08-20 15:43:29,959][__main__][INFO] - Starting iteration 485. +[2025-08-20 15:43:53,100][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:53,101][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:53,108][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:55,563][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:55,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:55,571][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:43:55,573][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:43:55,574][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:43:55,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:56,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:57,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:58,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:59,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:43:59,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:00,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:01,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:02,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:03,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:03,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:04,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:05,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:06,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:06,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:07,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:08,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:09,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:10,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:10,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:11,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:12,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:13,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:14,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:15,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:16,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:16,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:17,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:18,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:19,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:20,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:20,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:22,520][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:44:23,473][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:44:23,474][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:44:24,992][__main__][INFO] - Iteration 486 took 55s (37.61% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 43m 19s. Estimated total time: 15h 17m 13s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 36s. +[2025-08-20 15:44:24,994][__main__][INFO] - Starting iteration 486. +[2025-08-20 15:44:48,057][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:44:48,059][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:44:48,065][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:44:50,523][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:44:50,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:44:50,531][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:44:50,533][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:44:50,534][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:44:50,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:51,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:52,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:53,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:53,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:54,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:55,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:56,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:57,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:57,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:58,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:44:59,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:00,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:01,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:01,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:02,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:03,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:04,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:05,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:05,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:07,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:07,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:08,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:09,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:10,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:11,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:11,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:12,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:13,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:14,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:15,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:15,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:17,538][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:45:18,478][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:45:18,480][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:45:19,832][__main__][INFO] - Iteration 487 took 54s (37.59% Gen, 62.40% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 39m 9s. Estimated total time: 15h 13m 58s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 23s, 500 more iterations: 7h 36m 59s. +[2025-08-20 15:45:19,834][__main__][INFO] - Starting iteration 487. +[2025-08-20 15:45:42,907][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:45:42,909][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:45:42,915][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:45:45,376][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:45:45,377][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:45:45,383][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:45:45,385][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:45:45,386][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:45:45,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:46,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:47,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:48,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:48,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:49,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:50,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:51,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:52,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:52,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:53,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:54,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:55,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:56,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:56,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:57,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:58,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:59,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:45:59,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:01,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:02,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:02,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:03,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:04,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:05,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:06,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:06,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:07,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:08,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:09,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:10,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:10,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:12,485][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:46:13,415][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:46:13,417][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:46:14,752][__main__][INFO] - Iteration 488 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 39m 34s. Estimated total time: 15h 15m 17s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 38s. +[2025-08-20 15:46:14,753][__main__][INFO] - Starting iteration 488. +[2025-08-20 15:46:38,193][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:46:38,194][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:46:38,200][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:46:40,660][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:46:40,661][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:46:40,667][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:46:40,669][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:46:40,670][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:46:40,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:41,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:42,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:43,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:44,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:44,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:45,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:46,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:47,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:48,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:48,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:49,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:50,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:51,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:52,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:52,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:53,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:54,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:55,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:56,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:57,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:58,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:58,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:46:59,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:00,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:01,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:02,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:02,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:03,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:04,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:05,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:06,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:07,648][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:47:08,603][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:47:08,604][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:47:09,984][__main__][INFO] - Iteration 489 took 55s (37.99% Gen, 62.01% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 43m 51s. Estimated total time: 15h 20m 29s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 14s. +[2025-08-20 15:47:09,985][__main__][INFO] - Starting iteration 489. +[2025-08-20 15:47:33,085][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:47:33,087][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:47:33,093][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:47:35,572][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:47:35,573][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:47:35,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:47:35,581][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:47:35,582][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:47:35,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:36,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:37,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:38,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:39,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:39,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:40,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:41,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:42,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:43,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:43,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:44,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:45,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:46,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:46,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:47,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:48,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:49,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:50,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:50,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:51,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:53,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:53,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:54,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:55,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:56,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:57,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:57,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:58,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:47:59,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:00,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:00,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:02,616][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:48:03,540][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:48:03,541][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:48:04,946][__main__][INFO] - Iteration 490 took 54s (37.55% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 38m 26s. Estimated total time: 15h 16m 0s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 0s. +[2025-08-20 15:48:04,948][__main__][INFO] - Starting iteration 490. +[2025-08-20 15:48:27,995][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:48:27,996][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:48:28,002][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:48:30,466][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:48:30,467][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:48:30,473][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:48:30,476][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:48:30,476][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:48:30,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:31,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:32,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:33,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:33,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:34,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:35,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:36,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:37,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:37,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:38,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:39,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:40,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:41,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:41,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:42,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:43,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:44,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:45,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:45,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:46,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:47,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:48,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:49,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:50,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:51,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:51,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:52,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:53,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:54,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:55,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:55,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:48:57,549][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:48:58,466][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:48:58,468][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:48:59,829][__main__][INFO] - Iteration 491 took 54s (37.51% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 36m 12s. Estimated total time: 15h 14m 41s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 20s. +[2025-08-20 15:48:59,831][__main__][INFO] - Starting iteration 491. +[2025-08-20 15:49:23,278][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:49:23,279][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:49:23,286][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:49:25,735][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:49:25,736][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:49:25,743][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:49:25,745][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:49:25,745][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:49:26,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:26,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:27,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:28,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:29,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:30,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:30,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:31,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:32,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:33,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:33,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:34,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:35,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:36,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:37,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:37,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:38,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:39,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:40,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:41,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:41,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:42,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:43,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:44,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:45,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:46,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:47,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:47,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:48,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:49,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:50,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:51,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:49:52,679][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:49:53,602][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:49:53,603][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:49:54,974][__main__][INFO] - Iteration 492 took 55s (38.07% Gen, 61.92% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 39m 38s. Estimated total time: 15h 19m 2s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 31s. +[2025-08-20 15:49:54,975][__main__][INFO] - Starting iteration 492. +[2025-08-20 15:50:18,020][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:50:18,021][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:50:18,027][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:50:20,479][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:50:20,480][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:50:20,487][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:50:20,489][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:50:20,489][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:50:20,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:21,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:22,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:23,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:23,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:24,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:25,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:26,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:27,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:27,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:28,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:29,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:30,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:31,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:31,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:32,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:33,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:34,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:35,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:35,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:36,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:38,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:38,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:39,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:40,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:41,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:41,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:42,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:43,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:44,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:45,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:45,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:50:47,611][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:50:48,550][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:50:48,552][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:50:49,904][__main__][INFO] - Iteration 493 took 54s (37.49% Gen, 62.51% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 35m 9s. Estimated total time: 15h 15m 28s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 44s. +[2025-08-20 15:50:49,905][__main__][INFO] - Starting iteration 493. +[2025-08-20 15:51:13,260][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:51:13,261][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:51:13,268][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:51:15,706][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:51:15,708][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:51:15,714][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:51:15,716][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:51:15,717][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:51:16,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:16,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:17,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:18,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:19,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:19,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:20,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:21,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:22,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:23,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:23,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:24,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:25,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:26,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:27,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:27,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:28,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:29,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:30,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:31,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:32,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:33,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:33,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:34,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:35,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:36,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:37,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:37,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:38,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:39,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:40,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:41,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:51:42,678][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:51:43,595][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:51:43,597][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:51:44,991][__main__][INFO] - Iteration 494 took 55s (37.94% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 36m 51s. Estimated total time: 15h 18m 5s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 2s. +[2025-08-20 15:51:44,993][__main__][INFO] - Starting iteration 494. +[2025-08-20 15:52:07,957][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:52:07,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:52:07,965][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:52:10,429][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:52:10,430][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:52:10,437][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:52:10,439][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:52:10,439][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:52:10,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:11,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:12,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:13,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:13,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:14,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:15,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:16,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:17,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:17,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:18,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:19,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:20,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:21,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:21,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:22,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:23,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:24,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:25,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:25,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:26,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:27,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:28,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:29,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:30,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:31,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:31,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:32,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:33,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:34,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:35,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:35,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:52:37,501][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:52:38,466][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:52:38,467][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:52:40,141][__main__][INFO] - Iteration 495 took 55s (37.19% Gen, 62.81% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 36m 58s. Estimated total time: 15h 19m 7s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 33s. +[2025-08-20 15:52:40,142][__main__][INFO] - Starting iteration 495. +[2025-08-20 15:53:03,283][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:03,285][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:03,291][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:05,750][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:05,751][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:05,758][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:05,760][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:53:05,760][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:53:06,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:06,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:07,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:08,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:09,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:10,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:10,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:11,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:12,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:13,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:13,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:14,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:15,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:16,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:17,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:17,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:18,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:19,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:20,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:21,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:21,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:22,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:24,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:24,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:25,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:26,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:27,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:27,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:28,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:29,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:30,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:31,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:53:32,795][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:53:34,349][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:53:34,351][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:53:35,940][__main__][INFO] - Iteration 496 took 55s (37.08% Gen, 62.91% Train). Generation: 20s, Training: 35s. Estimated remaining time: 7h 46m 52s. Estimated total time: 15h 29m 57s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 59s, 500 more iterations: 7h 44m 58s. +[2025-08-20 15:53:35,941][__main__][INFO] - Starting iteration 496. +[2025-08-20 15:53:58,988][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:58,990][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:53:58,996][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:01,430][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:01,431][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:01,438][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:01,440][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:54:01,441][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:54:01,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:02,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:03,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:04,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:04,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:05,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:06,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:07,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:08,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:08,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:09,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:10,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:11,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:12,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:12,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:13,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:14,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:15,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:16,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:17,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:18,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:18,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:19,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:20,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:21,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:21,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:22,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:23,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:24,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:25,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:25,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:26,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:28,449][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:54:29,365][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:54:29,366][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:54:30,727][__main__][INFO] - Iteration 497 took 54s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 29m 5s. Estimated total time: 15h 13m 4s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 18s, 500 more iterations: 7h 36m 32s. +[2025-08-20 15:54:30,728][__main__][INFO] - Starting iteration 497. +[2025-08-20 15:54:53,725][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:53,726][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:53,732][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:56,190][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:56,191][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:56,198][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:54:56,200][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:54:56,200][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:54:56,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:57,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:58,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:58,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:54:59,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:00,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:01,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:02,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:02,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:03,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:04,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:05,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:06,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:06,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:07,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:08,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:09,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:10,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:11,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:12,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:12,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:13,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:14,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:15,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:16,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:16,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:17,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:18,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:19,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:20,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:20,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:21,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:23,225][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:55:24,142][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:55:24,144][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:55:25,474][__main__][INFO] - Iteration 498 took 54s (37.49% Gen, 62.50% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 27m 31s. Estimated total time: 15h 12m 25s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 14s, 500 more iterations: 7h 36m 12s. +[2025-08-20 15:55:25,476][__main__][INFO] - Starting iteration 498. +[2025-08-20 15:55:49,088][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:55:49,089][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:55:49,095][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:55:51,543][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:55:51,544][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:55:51,551][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:55:51,553][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:55:51,553][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:55:51,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:52,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:53,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:54,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:55,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:55,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:56,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:57,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:58,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:58,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:55:59,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:00,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:01,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:02,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:02,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:03,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:04,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:05,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:06,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:07,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:08,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:09,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:09,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:10,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:11,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:12,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:12,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:13,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:14,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:15,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:16,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:16,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:18,592][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:56:19,528][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:56:19,530][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:56:20,900][__main__][INFO] - Iteration 499 took 55s (38.19% Gen, 61.80% Train). Generation: 21s, Training: 34s. Estimated remaining time: 7h 37m 54s. Estimated total time: 15h 23m 44s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 22s, 500 more iterations: 7h 41m 52s. +[2025-08-20 15:56:20,902][__main__][INFO] - Starting iteration 499. +[2025-08-20 15:56:46,391][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:56:46,393][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:56:46,399][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:56:48,844][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:56:48,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:56:48,852][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:56:48,854][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:56:48,855][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:56:49,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:49,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:50,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:51,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:52,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:53,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:53,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:54,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:55,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:56,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:57,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:57,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:58,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:56:59,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:00,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:01,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:01,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:02,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:03,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:04,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:05,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:06,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:07,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:07,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:08,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:09,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:10,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:10,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:11,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:12,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:13,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:14,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:15,834][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:57:16,770][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:57:16,771][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:57:18,104][__main__][INFO] - Iteration 500 took 57s (40.29% Gen, 59.71% Train). Generation: 23s, Training: 34s. Estimated remaining time: 8h 6m 34s. Estimated total time: 15h 53m 21s. Time estimates for 10 more iterations: 9m 32s, 100 more iterations: 1h 35m 20s, 500 more iterations: 7h 56m 40s. +[2025-08-20 15:57:18,106][__main__][INFO] - Starting iteration 500. +[2025-08-20 15:57:41,170][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:57:41,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:57:41,178][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:57:43,629][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:57:43,630][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:57:43,637][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:57:43,639][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:57:43,640][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:57:43,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:44,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:45,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:46,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:47,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:47,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:48,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:49,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:50,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:51,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:51,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:52,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:53,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:54,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:55,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:55,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:56,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:57,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:58,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:59,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:57:59,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:00,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:01,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:02,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:03,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:04,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:05,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:05,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:06,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:07,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:08,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:09,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:10,669][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:58:11,609][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:58:11,610][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:58:15,671][__main__][INFO] - Iteration 501 took 57s (35.84% Gen, 59.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8h 11m 40s. Estimated total time: 15h 59m 24s. Time estimates for 10 more iterations: 9m 35s, 100 more iterations: 1h 35m 56s, 500 more iterations: 7h 59m 42s. +[2025-08-20 15:58:15,672][__main__][INFO] - Starting iteration 501. +[2025-08-20 15:58:38,676][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:58:38,678][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:58:38,684][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:58:41,163][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:58:41,164][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:58:41,171][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:58:41,173][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:58:41,173][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:58:41,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:42,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:43,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:43,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:44,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:45,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:46,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:47,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:47,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:48,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:49,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:50,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:50,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:51,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:52,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:53,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:54,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:54,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:55,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:56,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:57,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:58,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:58:59,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:00,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:01,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:01,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:02,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:03,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:04,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:05,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:05,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:06,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:08,290][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 15:59:09,220][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 15:59:09,222][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 15:59:10,485][__main__][INFO] - Iteration 502 took 54s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 24m 52s. Estimated total time: 15h 13m 32s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 46s. +[2025-08-20 15:59:10,486][__main__][INFO] - Starting iteration 502. +[2025-08-20 15:59:33,640][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:59:33,641][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:59:33,647][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:59:36,083][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:59:36,084][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:59:36,090][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 15:59:36,093][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 15:59:36,093][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 15:59:36,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:37,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:37,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:38,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:39,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:40,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:41,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:41,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:42,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:43,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:44,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:45,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:45,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:46,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:47,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:48,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:49,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:50,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:51,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:51,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:52,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:53,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:54,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:55,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:55,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:56,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:57,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:58,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:59,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 15:59:59,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:00,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:01,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:03,109][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:00:04,008][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:00:04,009][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:00:05,920][__main__][INFO] - Iteration 503 took 55s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 34m 18s. Estimated total time: 15h 23m 53s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 23s, 500 more iterations: 7h 41m 56s. +[2025-08-20 16:00:05,921][__main__][INFO] - Starting iteration 503. +[2025-08-20 16:00:29,353][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:00:29,354][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:00:29,361][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:00:31,810][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:00:31,811][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:00:31,817][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:00:31,820][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:00:31,820][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:00:32,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:32,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:33,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:34,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:35,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:36,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:36,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:37,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:38,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:39,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:40,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:40,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:41,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:42,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:43,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:44,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:44,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:45,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:46,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:47,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:47,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:48,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:49,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:50,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:51,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:51,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:53,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:54,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:54,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:55,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:56,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:57,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:00:58,860][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:00:59,773][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:00:59,775][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:01:01,475][__main__][INFO] - Iteration 504 took 55s (37.78% Gen, 62.22% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 35m 22s. Estimated total time: 15h 25m 52s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 35s, 500 more iterations: 7h 42m 56s. +[2025-08-20 16:01:01,476][__main__][INFO] - Starting iteration 504. +[2025-08-20 16:01:24,427][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:01:24,428][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:01:24,435][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:01:26,892][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:01:26,893][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:01:26,900][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:01:26,901][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:01:26,902][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:01:27,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:27,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:28,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:29,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:30,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:31,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:31,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:32,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:33,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:34,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:35,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:35,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:36,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:37,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:38,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:39,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:39,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:40,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:41,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:42,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:43,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:43,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:44,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:45,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:46,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:47,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:48,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:49,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:49,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:50,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:51,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:52,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:01:53,957][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:01:54,886][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:01:54,888][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:01:56,162][__main__][INFO] - Iteration 505 took 54s (37.50% Gen, 62.50% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 19m 59s. Estimated total time: 15h 11m 25s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 8s, 500 more iterations: 7h 35m 42s. +[2025-08-20 16:01:56,163][__main__][INFO] - Starting iteration 505. +[2025-08-20 16:02:19,259][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:02:19,260][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:02:19,267][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:02:21,708][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:02:21,709][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:02:21,716][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:02:21,718][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:02:21,718][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:02:22,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:22,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:23,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:24,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:25,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:25,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:26,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:27,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:28,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:29,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:29,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:30,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:31,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:32,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:33,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:33,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:34,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:35,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:36,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:37,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:38,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:39,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:39,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:40,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:41,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:42,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:43,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:43,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:44,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:45,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:46,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:47,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:02:48,715][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:02:49,642][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:02:49,644][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:02:51,017][__main__][INFO] - Iteration 506 took 54s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 21m 53s. Estimated total time: 15h 14m 13s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 25s, 500 more iterations: 7h 37m 6s. +[2025-08-20 16:02:51,018][__main__][INFO] - Starting iteration 506. +[2025-08-20 16:03:14,049][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:03:14,050][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:03:14,057][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:03:16,512][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:03:16,513][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:03:16,520][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:03:16,522][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:03:16,523][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:03:16,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:17,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:18,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:19,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:19,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:20,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:21,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:22,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:23,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:23,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:24,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:25,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:26,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:27,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:27,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:28,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:29,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:30,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:31,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:32,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:33,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:34,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:34,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:35,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:36,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:37,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:37,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:38,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:39,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:40,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:41,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:41,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:03:43,584][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:03:44,598][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:03:44,600][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:03:45,949][__main__][INFO] - Iteration 507 took 54s (37.47% Gen, 62.53% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 22m 15s. Estimated total time: 15h 15m 30s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 45s. +[2025-08-20 16:03:45,951][__main__][INFO] - Starting iteration 507. +[2025-08-20 16:04:08,942][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:04:08,944][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:04:08,950][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:04:11,414][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:04:11,415][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:04:11,421][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:04:11,424][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:04:11,424][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:04:11,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:12,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:13,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:14,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:14,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:15,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:16,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:17,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:18,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:18,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:19,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:20,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:21,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:22,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:22,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:23,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:24,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:25,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:26,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:26,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:28,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:29,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:29,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:30,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:31,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:32,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:32,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:33,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:34,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:35,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:36,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:36,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:04:38,604][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:04:39,525][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:04:39,527][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:04:40,904][__main__][INFO] - Iteration 508 took 54s (37.38% Gen, 62.62% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 21m 42s. Estimated total time: 15h 15m 52s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 56s. +[2025-08-20 16:04:40,905][__main__][INFO] - Starting iteration 508. +[2025-08-20 16:05:04,178][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:05:04,180][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:05:04,186][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:05:06,649][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:05:06,650][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:05:06,657][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:05:06,659][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:05:06,660][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:05:06,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:07,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:08,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:09,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:10,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:10,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:11,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:12,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:13,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:14,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:14,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:15,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:16,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:17,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:18,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:18,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:19,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:20,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:21,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:22,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:23,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:24,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:24,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:25,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:26,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:27,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:28,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:28,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:29,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:30,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:31,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:32,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:05:33,710][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:05:35,730][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:05:35,732][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:05:37,512][__main__][INFO] - Iteration 509 took 56s (36.77% Gen, 63.22% Train). Generation: 20s, Training: 35s. Estimated remaining time: 7h 48m 19s. Estimated total time: 15h 43m 26s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 20s, 500 more iterations: 7h 51m 43s. +[2025-08-20 16:05:37,514][__main__][INFO] - Starting iteration 509. +[2025-08-20 16:06:00,552][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:06:00,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:06:00,560][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:06:03,011][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:06:03,012][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:06:03,019][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:06:03,021][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:06:03,022][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:06:03,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:04,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:04,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:05,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:06,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:07,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:08,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:08,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:09,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:10,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:11,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:12,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:12,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:13,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:14,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:15,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:16,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:16,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:17,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:18,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:19,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:20,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:21,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:22,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:22,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:23,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:24,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:25,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:26,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:26,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:27,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:28,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:06:30,062][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:06:38,917][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:06:38,919][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:06:40,424][__main__][INFO] - Iteration 510 took 1m 2s (32.73% Gen, 67.27% Train). Generation: 20s, Training: 42s. Estimated remaining time: 9h 32m 21s. Estimated total time: 17h 28m 30s. Time estimates for 10 more iterations: 10m 29s, 100 more iterations: 1h 44m 51s, 500 more iterations: 8h 44m 15s. +[2025-08-20 16:06:40,426][__main__][INFO] - Starting iteration 510. +[2025-08-20 16:07:03,230][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:03,231][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:03,237][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:05,696][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:05,697][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:05,704][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:05,706][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:07:05,707][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:07:06,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:06,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:07,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:08,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:09,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:09,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:10,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:11,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:12,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:13,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:13,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:14,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:15,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:16,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:17,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:17,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:18,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:19,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:20,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:21,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:21,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:22,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:23,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:24,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:25,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:26,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:27,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:27,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:28,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:29,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:30,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:31,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:07:32,724][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:07:33,644][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:07:33,645][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:07:34,913][__main__][INFO] - Iteration 511 took 54s (37.33% Gen, 62.66% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 11m 3s. Estimated total time: 15h 8m 6s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 48s, 500 more iterations: 7h 34m 3s. +[2025-08-20 16:07:34,915][__main__][INFO] - Starting iteration 511. +[2025-08-20 16:07:57,972][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:57,974][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:07:57,980][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:00,468][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:00,469][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:00,476][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:00,478][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:08:00,479][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:08:00,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:01,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:02,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:03,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:03,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:04,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:05,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:06,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:07,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:07,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:08,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:09,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:10,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:11,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:11,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:12,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:13,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:14,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:15,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:16,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:17,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:17,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:18,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:19,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:20,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:21,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:21,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:22,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:23,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:24,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:25,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:25,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:27,557][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:08:28,485][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:08:28,486][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:08:29,897][__main__][INFO] - Iteration 512 took 54s (37.44% Gen, 62.56% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 18m 22s. Estimated total time: 15h 16m 21s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 10s. +[2025-08-20 16:08:29,898][__main__][INFO] - Starting iteration 512. +[2025-08-20 16:08:53,002][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:53,003][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:53,010][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:55,485][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:55,486][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:55,492][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:08:55,494][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:08:55,495][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:08:55,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:56,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:57,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:58,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:58,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:08:59,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:00,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:01,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:02,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:02,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:03,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:04,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:05,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:06,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:06,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:07,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:08,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:09,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:10,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:10,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:12,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:12,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:13,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:14,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:15,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:16,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:16,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:17,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:18,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:19,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:20,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:20,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:22,512][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:09:23,415][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:09:23,417][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:09:24,772][__main__][INFO] - Iteration 513 took 54s (37.63% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 15m 39s. Estimated total time: 15h 14m 33s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 16s. +[2025-08-20 16:09:24,773][__main__][INFO] - Starting iteration 513. +[2025-08-20 16:09:47,868][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:09:47,875][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:09:47,886][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:09:50,367][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:09:50,369][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:09:50,375][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:09:50,377][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:09:50,378][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:09:50,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:51,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:52,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:53,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:53,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:54,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:55,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:56,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:57,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:57,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:58,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:09:59,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:00,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:01,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:01,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:02,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:03,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:04,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:05,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:05,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:06,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:07,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:08,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:09,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:10,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:11,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:11,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:12,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:13,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:14,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:15,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:15,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:17,512][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:10:18,461][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:10:18,462][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:10:20,886][__main__][INFO] - Iteration 514 took 56s (36.77% Gen, 63.23% Train). Generation: 20s, Training: 35s. Estimated remaining time: 7h 35m 22s. Estimated total time: 15h 35m 12s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 31s, 500 more iterations: 7h 47m 36s. +[2025-08-20 16:10:20,887][__main__][INFO] - Starting iteration 514. +[2025-08-20 16:10:44,095][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:10:44,097][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:10:44,103][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:10:46,540][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:10:46,542][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:10:46,548][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:10:46,550][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:10:46,551][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:10:46,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:47,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:48,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:49,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:50,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:50,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:51,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:52,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:53,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:53,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:54,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:55,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:56,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:57,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:57,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:58,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:10:59,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:00,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:01,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:02,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:03,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:04,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:04,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:05,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:06,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:07,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:07,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:08,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:09,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:10,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:11,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:11,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:13,628][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:11:14,569][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:11:14,570][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:11:15,921][__main__][INFO] - Iteration 515 took 55s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 16m 28s. Estimated total time: 15h 17m 13s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 36s. +[2025-08-20 16:11:15,923][__main__][INFO] - Starting iteration 515. +[2025-08-20 16:11:38,933][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:11:38,935][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:11:38,941][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:11:41,382][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:11:41,384][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:11:41,390][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:11:41,392][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:11:41,393][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:11:41,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:42,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:43,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:44,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:44,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:45,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:46,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:47,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:48,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:48,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:49,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:50,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:51,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:52,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:52,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:53,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:54,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:55,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:55,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:56,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:57,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:58,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:59,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:11:59,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:00,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:01,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:02,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:03,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:04,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:05,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:06,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:06,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:08,532][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:12:09,457][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:12:09,459][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:12:11,175][__main__][INFO] - Iteration 516 took 55s (37.24% Gen, 62.76% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 19m 11s. Estimated total time: 15h 20m 51s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 5s, 500 more iterations: 7h 40m 25s. +[2025-08-20 16:12:11,176][__main__][INFO] - Starting iteration 516. +[2025-08-20 16:12:33,994][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:12:33,995][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:12:34,002][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:12:36,460][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:12:36,461][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:12:36,467][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:12:36,470][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:12:36,470][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:12:36,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:37,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:38,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:39,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:39,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:40,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:41,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:42,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:43,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:43,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:44,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:45,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:46,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:47,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:47,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:48,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:49,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:50,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:51,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:51,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:52,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:54,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:54,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:55,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:56,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:57,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:58,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:58,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:12:59,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:00,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:01,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:02,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:03,672][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:13:04,620][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:13:04,621][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:13:05,906][__main__][INFO] - Iteration 517 took 54s (37.23% Gen, 62.76% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 9m 34s. Estimated total time: 15h 12m 9s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 12s, 500 more iterations: 7h 36m 4s. +[2025-08-20 16:13:05,908][__main__][INFO] - Starting iteration 517. +[2025-08-20 16:13:28,765][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:13:28,766][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:13:28,773][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:13:31,212][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:13:31,213][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:13:31,220][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:13:31,222][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:13:31,222][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:13:31,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:32,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:33,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:33,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:34,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:35,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:36,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:37,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:37,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:38,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:39,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:40,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:41,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:41,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:42,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:43,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:44,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:45,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:46,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:47,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:47,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:48,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:49,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:50,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:51,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:51,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:52,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:53,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:54,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:55,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:55,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:56,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:13:58,258][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:13:59,190][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:13:59,191][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:14:00,595][__main__][INFO] - Iteration 518 took 54s (37.33% Gen, 62.66% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 7m 56s. Estimated total time: 15h 11m 26s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 8s, 500 more iterations: 7h 35m 43s. +[2025-08-20 16:14:00,596][__main__][INFO] - Starting iteration 518. +[2025-08-20 16:14:23,711][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:14:23,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:14:23,718][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:14:26,193][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:14:26,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:14:26,201][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:14:26,203][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:14:26,204][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:14:26,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:27,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:28,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:28,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:29,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:30,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:31,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:32,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:32,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:33,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:34,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:35,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:36,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:36,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:37,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:38,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:39,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:40,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:40,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:41,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:42,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:43,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:43,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:45,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:46,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:46,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:47,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:48,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:49,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:50,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:50,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:51,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:14:53,210][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:14:54,168][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:14:54,169][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:14:55,597][__main__][INFO] - Iteration 519 took 55s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 12m 15s. Estimated total time: 15h 16m 40s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 20s. +[2025-08-20 16:14:55,598][__main__][INFO] - Starting iteration 519. +[2025-08-20 16:15:18,783][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:15:18,785][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:15:18,791][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:15:21,236][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:15:21,237][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:15:21,244][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:15:21,246][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:15:21,246][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:15:21,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:22,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:23,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:23,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:24,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:25,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:26,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:27,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:27,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:28,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:29,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:30,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:31,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:31,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:32,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:33,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:34,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:35,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:35,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:36,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:38,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:38,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:39,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:40,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:41,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:42,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:42,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:43,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:44,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:45,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:45,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:46,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:15:48,426][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:15:49,361][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:15:49,362][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:15:50,642][__main__][INFO] - Iteration 520 took 55s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 12m 3s. Estimated total time: 15h 17m 23s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 44s, 500 more iterations: 7h 38m 41s. +[2025-08-20 16:15:50,644][__main__][INFO] - Starting iteration 520. +[2025-08-20 16:16:13,595][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:16:13,596][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:16:13,602][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:16:16,044][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:16:16,046][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:16:16,052][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:16:16,054][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:16:16,055][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:16:16,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:17,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:17,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:18,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:19,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:20,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:21,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:21,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:22,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:23,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:24,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:25,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:25,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:26,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:27,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:28,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:29,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:29,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:30,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:31,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:32,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:33,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:34,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:35,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:35,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:36,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:37,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:38,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:39,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:39,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:40,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:41,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:16:43,091][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:16:44,035][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:16:44,036][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:16:45,471][__main__][INFO] - Iteration 521 took 54s (37.39% Gen, 62.61% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 7m 32s. Estimated total time: 15h 13m 47s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 53s. +[2025-08-20 16:16:45,473][__main__][INFO] - Starting iteration 521. +[2025-08-20 16:17:08,336][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:17:08,338][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:17:08,344][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:17:10,776][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:17:10,777][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:17:10,784][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:17:10,786][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:17:10,786][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:17:11,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:11,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:12,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:13,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:14,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:15,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:15,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:16,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:17,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:18,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:19,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:19,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:20,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:21,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:22,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:22,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:23,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:24,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:25,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:26,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:26,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:27,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:28,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:29,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:30,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:31,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:32,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:33,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:33,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:34,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:35,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:36,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:17:37,826][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:17:38,799][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:17:38,801][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:17:40,196][__main__][INFO] - Iteration 522 took 54s (37.31% Gen, 62.69% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 4m 53s. Estimated total time: 15h 12m 2s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 12s, 500 more iterations: 7h 36m 1s. +[2025-08-20 16:17:40,197][__main__][INFO] - Starting iteration 522. +[2025-08-20 16:18:03,071][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:03,072][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:03,078][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:05,508][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:05,510][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:05,516][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:05,518][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:18:05,519][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:18:05,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:06,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:07,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:08,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:08,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:09,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:10,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:11,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:12,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:12,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:13,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:14,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:15,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:16,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:16,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:17,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:18,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:19,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:20,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:20,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:22,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:22,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:23,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:24,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:25,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:26,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:26,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:27,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:28,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:29,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:30,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:30,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:18:32,592][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:18:33,512][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:18:33,513][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:18:35,001][__main__][INFO] - Iteration 523 took 54s (37.27% Gen, 62.73% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 5m 18s. Estimated total time: 15h 13m 22s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 41s. +[2025-08-20 16:18:35,002][__main__][INFO] - Starting iteration 523. +[2025-08-20 16:18:58,161][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:58,162][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:18:58,169][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:00,615][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:00,616][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:00,623][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:00,625][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:19:00,625][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:19:00,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:01,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:02,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:03,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:04,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:04,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:05,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:06,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:07,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:08,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:08,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:09,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:10,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:11,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:12,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:12,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:13,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:14,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:15,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:16,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:16,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:17,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:18,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:19,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:20,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:20,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:21,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:22,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:23,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:24,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:25,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:26,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:27,630][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:19:28,550][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:19:28,552][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:19:29,925][__main__][INFO] - Iteration 524 took 54s (37.71% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 6m 23s. Estimated total time: 15h 15m 22s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 41s. +[2025-08-20 16:19:29,926][__main__][INFO] - Starting iteration 524. +[2025-08-20 16:19:52,881][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:52,883][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:52,889][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:55,340][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:55,341][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:55,348][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:19:55,350][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:19:55,351][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:19:55,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:56,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:57,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:58,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:58,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:19:59,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:00,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:01,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:01,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:02,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:03,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:04,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:05,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:05,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:06,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:07,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:08,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:09,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:10,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:11,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:12,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:12,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:13,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:14,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:15,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:16,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:16,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:17,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:18,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:19,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:20,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:20,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:22,412][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:20:23,331][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:20:23,332][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:20:24,840][__main__][INFO] - Iteration 525 took 54s (37.34% Gen, 62.66% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 5m 19s. Estimated total time: 15h 15m 12s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 36s. +[2025-08-20 16:20:24,841][__main__][INFO] - Starting iteration 525. +[2025-08-20 16:20:47,680][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:20:47,681][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:20:47,687][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:20:50,139][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:20:50,141][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:20:50,147][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:20:50,149][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:20:50,150][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:20:50,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:51,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:52,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:52,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:53,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:54,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:55,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:55,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:56,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:57,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:58,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:59,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:20:59,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:00,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:01,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:02,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:03,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:03,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:04,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:05,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:06,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:07,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:08,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:09,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:10,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:10,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:11,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:12,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:13,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:13,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:14,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:15,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:17,204][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:21:18,217][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:21:18,219][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:21:19,555][__main__][INFO] - Iteration 526 took 54s (37.30% Gen, 62.70% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 1m 4s. Estimated total time: 15h 11m 53s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 11s, 500 more iterations: 7h 35m 56s. +[2025-08-20 16:21:19,556][__main__][INFO] - Starting iteration 526. +[2025-08-20 16:21:42,857][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:21:42,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:21:42,865][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:21:45,296][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:21:45,297][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:21:45,304][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:21:45,306][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:21:45,306][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:21:45,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:46,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:47,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:47,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:48,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:49,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:50,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:51,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:51,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:52,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:53,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:54,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:21:55,147][mllm.training.trainer_common][INFO] - 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Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:03,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:04,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:05,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:05,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:06,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:07,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:08,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:09,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:09,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:10,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:12,274][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:22:13,251][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:22:13,253][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:22:14,547][__main__][INFO] - Iteration 527 took 54s (37.94% Gen, 62.05% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 4m 46s. Estimated total time: 15h 16m 30s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 15s. +[2025-08-20 16:22:14,548][__main__][INFO] - Starting iteration 527. +[2025-08-20 16:22:37,484][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:22:37,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:22:37,492][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:22:39,932][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:22:39,933][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:22:39,939][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:22:39,941][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:22:39,942][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:22:40,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:41,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:41,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:42,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:43,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:44,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:45,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:45,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:46,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:47,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:48,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:48,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:49,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:50,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:51,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:52,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:53,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:54,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:55,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:55,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:56,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:57,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:58,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:59,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:22:59,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:00,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:01,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:02,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:03,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:03,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:04,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:05,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:06,978][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:23:07,907][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:23:07,909][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:23:09,176][__main__][INFO] - Iteration 528 took 54s (37.52% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 57m 49s. Estimated total time: 15h 10m 27s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 2s, 500 more iterations: 7h 35m 13s. +[2025-08-20 16:23:09,177][__main__][INFO] - Starting iteration 528. +[2025-08-20 16:23:32,448][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:23:32,450][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:23:32,456][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:23:34,904][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:23:34,905][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:23:34,912][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:23:34,914][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:23:34,915][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:23:35,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:36,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:36,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:37,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:38,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:39,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:39,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:40,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:41,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:42,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:43,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:43,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:44,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:45,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:46,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:47,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:47,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:48,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:49,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:50,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:51,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:52,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:53,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:54,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:54,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:55,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:56,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:57,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:58,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:58,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:23:59,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:00,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:02,019][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:24:02,923][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:24:02,925][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:24:04,332][__main__][INFO] - Iteration 529 took 55s (37.76% Gen, 62.24% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 5m 40s. Estimated total time: 15h 19m 13s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 36s. +[2025-08-20 16:24:04,333][__main__][INFO] - Starting iteration 529. +[2025-08-20 16:24:27,491][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:24:27,493][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:24:27,499][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:24:29,939][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:24:29,940][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:24:29,947][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:24:29,949][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:24:29,950][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:24:30,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:31,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:31,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:32,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:33,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:34,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:35,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:35,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:36,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:37,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:38,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:38,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:39,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:40,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:41,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:42,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:42,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:43,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:44,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:45,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:46,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:46,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:48,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:48,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:49,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:50,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:51,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:52,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:52,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:53,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:54,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:55,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:24:56,910][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:24:57,846][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:24:57,847][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:24:59,192][__main__][INFO] - Iteration 530 took 54s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 59m 50s. Estimated total time: 15h 14m 18s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 25s, 500 more iterations: 7h 37m 9s. +[2025-08-20 16:24:59,194][__main__][INFO] - Starting iteration 530. +[2025-08-20 16:25:22,092][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:25:22,093][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:25:22,099][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:25:24,567][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:25:24,569][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:25:24,575][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:25:24,578][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:25:24,578][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:25:24,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:25,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:26,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:27,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:28,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:28,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:29,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:30,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:31,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:32,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:32,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:33,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:34,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:35,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:35,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:36,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:37,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:38,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:39,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:40,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:41,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:42,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:42,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:43,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:44,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:45,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:46,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:46,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:47,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:48,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:49,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:50,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:25:51,611][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:25:52,530][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:25:52,531][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:25:53,885][__main__][INFO] - Iteration 531 took 54s (37.38% Gen, 62.62% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 56m 8s. Estimated total time: 15h 11m 30s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 9s, 500 more iterations: 7h 35m 45s. +[2025-08-20 16:25:53,887][__main__][INFO] - Starting iteration 531. +[2025-08-20 16:26:16,724][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:26:16,725][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:26:16,732][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:26:19,169][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:26:19,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:26:19,177][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:26:19,180][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:26:19,181][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:26:19,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:20,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:21,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:21,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:22,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:23,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:24,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:25,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:25,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:26,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:27,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:28,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:29,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:29,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:30,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:31,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:32,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:32,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:33,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:34,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:35,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:36,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:37,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:38,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:39,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:39,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:40,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:41,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:42,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:43,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:43,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:44,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:26:46,221][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:26:47,150][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:26:47,151][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:26:48,508][__main__][INFO] - Iteration 532 took 54s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 54m 3s. Estimated total time: 15h 10m 21s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 2s, 500 more iterations: 7h 35m 10s. +[2025-08-20 16:26:48,510][__main__][INFO] - Starting iteration 532. +[2025-08-20 16:27:11,378][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:27:11,379][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:27:11,385][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:27:13,834][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:27:13,835][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:27:13,841][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:27:13,844][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:27:13,844][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:27:14,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:14,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:15,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:16,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:17,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:18,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:18,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:19,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:20,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:21,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:22,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:22,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:23,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:24,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:25,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:26,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:26,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:27,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:28,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:29,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:30,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:31,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:32,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:32,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:33,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:34,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:35,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:36,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:36,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:37,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:38,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:39,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:27:40,821][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:27:41,729][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:27:41,731][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:27:43,102][__main__][INFO] - Iteration 533 took 54s (37.42% Gen, 62.58% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 52m 40s. Estimated total time: 15h 9m 52s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 59s, 500 more iterations: 7h 34m 56s. +[2025-08-20 16:27:43,104][__main__][INFO] - Starting iteration 533. +[2025-08-20 16:28:06,229][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:28:06,230][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:28:06,236][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:28:08,683][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:28:08,684][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:28:08,691][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:28:08,693][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:28:08,694][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:28:08,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:09,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:10,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:11,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:12,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:12,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:13,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:14,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:15,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:16,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:16,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:17,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:18,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:19,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:20,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:20,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:21,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:22,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:23,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:24,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:24,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:25,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:26,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:27,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:28,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:29,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:30,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:30,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:31,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:32,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:33,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:34,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:28:35,735][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:28:36,670][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:28:36,671][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:28:37,890][__main__][INFO] - Iteration 534 took 54s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 54m 58s. Estimated total time: 15h 13m 5s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 18s, 500 more iterations: 7h 36m 32s. +[2025-08-20 16:28:37,892][__main__][INFO] - Starting iteration 534. +[2025-08-20 16:29:00,844][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:00,845][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:00,851][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:03,305][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:03,307][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:03,313][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:03,316][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:29:03,316][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:29:03,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:04,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:05,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:05,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:06,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:07,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:08,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:09,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:09,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:10,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:11,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:12,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:13,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:13,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:14,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:15,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:16,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:17,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:17,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:18,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:19,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:20,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:21,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:22,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:23,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:23,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:24,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:25,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:26,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:27,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:27,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:28,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:30,306][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:29:31,221][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:29:31,223][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:29:32,600][__main__][INFO] - Iteration 535 took 54s (37.47% Gen, 62.53% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 52m 46s. Estimated total time: 15h 11m 48s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 10s, 500 more iterations: 7h 35m 54s. +[2025-08-20 16:29:32,602][__main__][INFO] - Starting iteration 535. +[2025-08-20 16:29:55,428][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:55,429][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:55,436][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:57,903][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:57,905][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:57,911][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:29:57,913][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:29:57,914][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:29:58,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:59,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:29:59,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:00,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:01,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:02,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:02,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:03,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:04,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:05,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:06,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:06,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:07,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:08,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:09,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:10,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:10,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:11,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:12,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:13,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:14,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:15,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:16,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:16,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:17,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:18,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:19,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:20,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:20,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:21,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:22,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:23,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:24,896][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:30:25,864][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:30:25,865][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:30:27,226][__main__][INFO] - Iteration 536 took 54s (37.28% Gen, 62.72% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 50m 28s. Estimated total time: 15h 10m 24s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 2s, 500 more iterations: 7h 35m 12s. +[2025-08-20 16:30:27,228][__main__][INFO] - Starting iteration 536. +[2025-08-20 16:30:50,122][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:30:50,124][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:30:50,130][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:30:52,591][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:30:52,592][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:30:52,598][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:30:52,601][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:30:52,601][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:30:52,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:53,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:54,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:55,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:56,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:56,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:57,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:58,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:30:59,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:00,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:00,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:01,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:02,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:03,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:04,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:04,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:05,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:06,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:07,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:08,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:09,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:10,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:10,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:11,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:12,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:13,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:14,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:14,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:15,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:16,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:17,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:18,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:19,620][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:31:20,534][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:31:20,535][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:31:22,006][__main__][INFO] - Iteration 537 took 54s (37.32% Gen, 62.68% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 52m 6s. Estimated total time: 15h 12m 57s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 17s, 500 more iterations: 7h 36m 28s. +[2025-08-20 16:31:22,007][__main__][INFO] - Starting iteration 537. +[2025-08-20 16:31:44,999][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:31:45,000][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:31:45,007][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:31:47,461][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:31:47,462][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:31:47,469][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:31:47,471][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:31:47,472][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:31:47,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:48,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:49,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:50,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:50,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:51,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:52,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:53,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:54,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:54,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:55,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:56,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:57,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:58,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:58,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:31:59,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:00,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:01,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:02,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:02,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:03,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:04,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:05,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:06,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:07,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:08,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:08,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:09,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:10,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:11,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:12,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:12,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:14,522][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:32:15,426][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:32:15,427][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:32:16,740][__main__][INFO] - Iteration 538 took 54s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 50m 26s. Estimated total time: 15h 12m 12s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 13s, 500 more iterations: 7h 36m 6s. +[2025-08-20 16:32:16,742][__main__][INFO] - Starting iteration 538. +[2025-08-20 16:32:40,033][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:32:40,034][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:32:40,041][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:32:42,505][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:32:42,507][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:32:42,513][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:32:42,515][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:32:42,516][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:32:42,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:43,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:44,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:45,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:45,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:46,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:47,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:48,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:49,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:49,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:50,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:51,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:52,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:53,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:53,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:54,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:55,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:56,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:57,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:57,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:58,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:32:59,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:00,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:01,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:02,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:03,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:03,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:04,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:05,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:06,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:07,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:07,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:09,481][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:33:10,502][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:33:10,505][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:33:11,930][__main__][INFO] - Iteration 539 took 55s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 57m 6s. Estimated total time: 15h 19m 47s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 53s. +[2025-08-20 16:33:11,932][__main__][INFO] - Starting iteration 539. +[2025-08-20 16:33:34,998][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:33:35,000][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:33:35,006][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:33:37,484][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:33:37,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:33:37,492][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:33:37,676][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:33:37,677][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:33:37,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:38,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:39,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:40,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:41,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:41,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:42,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:43,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:44,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:45,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:45,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:46,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:47,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:48,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:49,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:50,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:54,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:56,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:57,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:57,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:58,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:33:59,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:00,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:01,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:02,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:03,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:03,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:04,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:05,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:06,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:07,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:07,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:09,515][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:31, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:34:10,425][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:34:10,426][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:34:11,714][__main__][INFO] - Iteration 540 took 59s (34.43% Gen, 65.56% Train). Generation: 20s, Training: 39s. Estimated remaining time: 8h 12m 41s. Estimated total time: 16h 36m 22s. Time estimates for 10 more iterations: 9m 57s, 100 more iterations: 1h 39m 38s, 500 more iterations: 8h 18m 11s. +[2025-08-20 16:34:11,716][__main__][INFO] - Starting iteration 540. +[2025-08-20 16:34:35,141][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:34:35,142][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:34:35,148][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:34:37,614][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:34:37,615][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:34:37,622][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:34:37,624][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:34:37,625][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:34:37,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:38,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:39,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:40,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:41,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:41,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:42,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:43,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:44,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:45,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:45,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:46,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:47,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:48,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:49,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:49,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:50,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:51,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:52,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:53,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:54,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:55,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:55,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:56,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:57,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:58,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:59,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:34:59,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:00,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:01,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:02,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:02,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:04,589][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:35:05,511][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:35:05,512][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:35:06,840][__main__][INFO] - Iteration 541 took 55s (38.03% Gen, 61.97% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 54m 8s. Estimated total time: 15h 18m 43s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 21s. +[2025-08-20 16:35:06,842][__main__][INFO] - Starting iteration 541. +[2025-08-20 16:35:30,488][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:35:30,489][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:35:30,496][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:35:32,964][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:35:32,965][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:35:32,971][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:35:32,974][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:35:32,974][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:35:33,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:34,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:34,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:35,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:36,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:37,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:38,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:38,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:39,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:40,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:41,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:42,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:42,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:43,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:44,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:45,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:46,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:47,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:47,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:48,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:49,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:50,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:51,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:51,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:52,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:53,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:54,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:55,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:55,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:56,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:57,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:58,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:35:59,932][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:36:00,877][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:36:00,878][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:36:02,339][__main__][INFO] - Iteration 542 took 55s (38.15% Gen, 61.84% Train). Generation: 21s, Training: 34s. Estimated remaining time: 6h 59m 25s. Estimated total time: 15h 24m 57s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 29s, 500 more iterations: 7h 42m 28s. +[2025-08-20 16:36:02,341][__main__][INFO] - Starting iteration 542. +[2025-08-20 16:36:25,401][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:36:25,402][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:36:25,409][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:36:27,894][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:36:27,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:36:27,901][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:36:27,904][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:36:27,904][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:36:28,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:28,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:29,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:30,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:31,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:32,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:32,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:33,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:34,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:35,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:36,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:36,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:37,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:38,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:39,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:40,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:40,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:41,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:43,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:43,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:44,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:45,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:46,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:47,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:47,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:48,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:49,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:50,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:51,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:51,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:52,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:53,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:36:55,007][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:36:55,938][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:36:55,939][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:36:57,313][__main__][INFO] - Iteration 543 took 54s (37.46% Gen, 62.54% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 49m 45s. Estimated total time: 15h 16m 12s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 6s. +[2025-08-20 16:36:57,315][__main__][INFO] - Starting iteration 543. +[2025-08-20 16:37:24,698][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:37:24,699][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:37:24,706][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:37:27,182][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:37:27,184][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:37:27,190][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:37:27,193][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:37:27,193][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:37:27,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:28,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:29,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:29,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:30,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:31,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:32,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:33,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:33,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:34,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:35,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:36,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:37,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:37,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:38,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:39,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:40,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:40,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:41,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:42,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:43,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:44,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:44,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:45,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:46,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:47,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:48,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:49,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:50,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:50,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:51,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:52,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:37:54,157][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:37:55,146][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:37:55,148][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:37:56,522][__main__][INFO] - Iteration 544 took 59s (42.10% Gen, 57.90% Train). Generation: 24s, Training: 34s. Estimated remaining time: 7h 59m 21s. Estimated total time: 16h 26m 46s. Time estimates for 10 more iterations: 9m 52s, 100 more iterations: 1h 38m 40s, 500 more iterations: 8h 13m 23s. +[2025-08-20 16:37:56,524][__main__][INFO] - Starting iteration 544. +[2025-08-20 16:38:19,525][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:38:19,527][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:38:19,533][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:38:21,983][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:38:21,985][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:38:21,991][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:38:21,994][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:38:21,994][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:38:22,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:23,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:23,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:24,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:25,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:26,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:27,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:27,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:28,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:29,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:30,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:31,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:31,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:32,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:33,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:34,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:35,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:35,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:36,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:37,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:38,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:39,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:40,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:41,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:41,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:42,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:43,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:44,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:44,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:45,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:46,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:47,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:38:48,944][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:38:49,865][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:38:49,866][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:38:51,235][__main__][INFO] - Iteration 545 took 54s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 43m 31s. Estimated total time: 15h 11m 51s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 11s, 500 more iterations: 7h 35m 55s. +[2025-08-20 16:38:51,237][__main__][INFO] - Starting iteration 545. +[2025-08-20 16:39:14,141][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:39:14,143][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:39:14,149][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:39:16,603][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:39:16,604][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:39:16,610][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:39:16,613][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:39:16,613][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:39:16,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:17,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:18,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:19,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:20,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:20,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:21,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:22,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:23,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:24,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:24,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:25,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:26,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:27,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:28,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:28,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:29,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:30,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:31,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:32,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:33,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:34,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:34,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:35,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:36,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:37,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:38,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:38,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:39,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:40,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:41,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:42,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:39:43,625][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:39:44,708][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:39:44,711][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:39:46,015][__main__][INFO] - Iteration 546 took 54s (37.37% Gen, 62.63% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 43m 42s. Estimated total time: 15h 12m 57s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 17s, 500 more iterations: 7h 36m 28s. +[2025-08-20 16:39:46,017][__main__][INFO] - Starting iteration 546. +[2025-08-20 16:40:08,992][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:40:08,993][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:40:08,999][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:40:11,464][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:40:11,465][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:40:11,471][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:40:11,473][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:40:11,474][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:40:11,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:12,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:13,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:14,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:14,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:15,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:16,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:17,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:18,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:18,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:19,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:20,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:21,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:22,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:22,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:23,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:24,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:25,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:26,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:26,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:27,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:28,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:29,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:30,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:31,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:32,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:32,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:33,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:34,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:35,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:36,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:36,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:40:38,466][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:40:39,394][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:40:39,396][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:40:40,705][__main__][INFO] - Iteration 547 took 54s (37.52% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 41m 18s. Estimated total time: 15h 11m 28s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 8s, 500 more iterations: 7h 35m 44s. +[2025-08-20 16:40:40,707][__main__][INFO] - Starting iteration 547. +[2025-08-20 16:41:03,818][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:03,820][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:03,826][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:06,257][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:06,258][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:06,264][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:06,267][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:41:06,267][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:41:06,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:07,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:08,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:08,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:09,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:10,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:11,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:12,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:12,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:13,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:14,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:15,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:16,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:16,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:17,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:18,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:19,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:20,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:21,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:22,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:22,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:23,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:24,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:25,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:26,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:26,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:27,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:28,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:29,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:30,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:30,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:31,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:41:33,168][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:41:34,123][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:41:34,126][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:41:35,605][__main__][INFO] - Iteration 548 took 54s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 43m 52s. Estimated total time: 15h 14m 57s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 28s. +[2025-08-20 16:41:35,607][__main__][INFO] - Starting iteration 548. +[2025-08-20 16:41:59,046][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:59,048][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:41:59,054][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:01,500][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:01,501][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:01,508][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:01,510][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:42:01,510][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:42:01,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:02,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:03,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:04,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:04,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:05,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:06,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:07,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:08,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:08,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:09,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:10,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:11,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:12,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:12,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:13,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:14,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:15,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:16,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:16,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:17,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:18,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:19,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:20,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:20,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:21,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:22,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:23,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:24,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:25,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:26,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:26,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:28,562][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:42:30,182][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:42:30,184][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:42:31,623][__main__][INFO] - Iteration 549 took 56s (37.47% Gen, 62.53% Train). Generation: 20s, Training: 35s. Estimated remaining time: 7h 1m 35s. Estimated total time: 15h 33m 36s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 21s, 500 more iterations: 7h 46m 48s. +[2025-08-20 16:42:31,625][__main__][INFO] - Starting iteration 549. +[2025-08-20 16:42:55,016][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:55,018][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:55,024][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:57,473][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:57,474][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:57,481][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:42:57,483][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:42:57,484][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:42:57,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:58,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:42:59,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:00,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:00,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:01,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:02,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:03,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:04,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:04,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:05,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:06,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:07,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:08,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:08,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:09,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:10,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:11,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:12,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:12,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:13,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:14,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:15,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:16,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:17,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:18,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:18,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:19,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:20,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:21,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:22,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:22,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:24,524][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:43:25,457][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:43:25,459][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:43:26,842][__main__][INFO] - Iteration 550 took 55s (37.93% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 47m 21s. Estimated total time: 15h 20m 16s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 1s, 500 more iterations: 7h 40m 8s. +[2025-08-20 16:43:26,844][__main__][INFO] - Starting iteration 550. +[2025-08-20 16:43:49,835][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:43:49,836][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:43:49,842][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:43:52,293][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:43:52,295][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:43:52,301][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:43:52,304][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:43:52,304][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:43:52,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:53,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:54,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:54,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:55,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:56,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:57,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:58,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:58,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:43:59,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:00,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:01,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:02,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:02,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:03,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:04,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:05,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:06,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:06,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:07,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:08,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:09,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:10,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:11,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:12,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:12,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:13,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:14,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:15,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:16,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:16,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:17,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:19,206][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:44:20,316][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:44:20,318][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:44:24,365][__main__][INFO] - Iteration 551 took 57s (35.68% Gen, 59.72% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7h 24m 48s. Estimated total time: 15h 58m 41s. Time estimates for 10 more iterations: 9m 35s, 100 more iterations: 1h 35m 52s, 500 more iterations: 7h 59m 20s. +[2025-08-20 16:44:24,367][__main__][INFO] - Starting iteration 551. +[2025-08-20 16:44:47,492][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:44:47,493][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:44:47,499][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:44:49,952][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:44:49,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:44:49,960][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:44:49,963][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:44:49,963][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:44:50,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:51,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:51,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:52,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:53,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:54,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:55,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:55,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:56,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:57,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:58,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:58,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:44:59,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:00,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:01,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:02,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:02,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:03,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:04,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:05,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:06,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:06,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:07,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:09,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:09,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:10,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:11,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:12,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:12,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:13,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:14,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:15,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:16,918][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:45:17,864][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:45:17,865][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:45:19,241][__main__][INFO] - Iteration 552 took 54s (37.68% Gen, 62.32% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 39m 45s. Estimated total time: 15h 14m 33s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 16s. +[2025-08-20 16:45:19,243][__main__][INFO] - Starting iteration 552. +[2025-08-20 16:45:42,489][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:45:42,490][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:45:42,496][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:45:44,963][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:45:44,964][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:45:44,970][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:45:44,973][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:45:44,973][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:45:45,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:46,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:46,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:47,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:48,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:49,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:50,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:50,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:51,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:52,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:53,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:54,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:54,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:55,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:56,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:57,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:57,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:59,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:45:59,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:00,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:01,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:02,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:03,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:03,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:04,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:05,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:06,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:07,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:07,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:08,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:09,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:10,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:11,973][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:46:12,903][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:46:12,904][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:46:14,305][__main__][INFO] - Iteration 553 took 55s (37.73% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 41m 58s. Estimated total time: 15h 17m 41s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 50s. +[2025-08-20 16:46:14,307][__main__][INFO] - Starting iteration 553. +[2025-08-20 16:46:37,434][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:46:37,436][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:46:37,442][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:46:39,909][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:46:39,910][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:46:39,917][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:46:39,919][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:46:39,920][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:46:40,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:41,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:41,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:42,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:43,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:44,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:44,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:45,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:46,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:47,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:48,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:48,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:49,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:50,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:51,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:52,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:52,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:53,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:54,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:55,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:56,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:56,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:57,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:58,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:46:59,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:00,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:01,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:02,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:02,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:03,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:04,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:05,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:06,847][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:47:07,821][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:47:07,823][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:47:09,153][__main__][INFO] - Iteration 554 took 54s (37.67% Gen, 62.33% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 37m 27s. Estimated total time: 15h 14m 5s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 2s. +[2025-08-20 16:47:09,154][__main__][INFO] - Starting iteration 554. +[2025-08-20 16:47:32,570][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:47:32,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:47:32,577][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:47:35,058][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:47:35,059][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:47:35,066][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:47:35,068][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:47:35,069][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:47:35,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:36,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:36,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:37,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:38,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:39,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:40,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:40,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:41,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:42,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:43,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:44,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:44,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:45,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:46,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:47,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:48,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:48,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:49,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:50,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:51,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:52,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:52,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:53,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:54,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:55,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:56,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:57,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:58,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:58,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:47:59,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:00,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:02,108][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:48:03,065][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:48:03,067][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:48:04,319][__main__][INFO] - Iteration 555 took 55s (37.98% Gen, 62.02% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 41m 50s. Estimated total time: 15h 19m 24s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 56s, 500 more iterations: 7h 39m 42s. +[2025-08-20 16:48:04,320][__main__][INFO] - Starting iteration 555. +[2025-08-20 16:48:27,388][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:48:27,390][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:48:27,396][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:48:29,824][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:48:29,825][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:48:29,831][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:48:29,833][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:48:29,834][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:48:30,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:30,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:31,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:32,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:33,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:34,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:34,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:35,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:36,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:37,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:38,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:38,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:39,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:40,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:41,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:42,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:42,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:43,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:44,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:45,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:45,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:46,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:47,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:48,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:49,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:50,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:51,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:52,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:52,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:53,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:54,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:55,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:48:56,826][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:48:57,834][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:48:57,836][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:48:59,394][__main__][INFO] - Iteration 556 took 55s (37.47% Gen, 62.53% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 39m 25s. Estimated total time: 15h 17m 53s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 56s. +[2025-08-20 16:48:59,396][__main__][INFO] - Starting iteration 556. +[2025-08-20 16:49:22,417][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:49:22,419][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:49:22,425][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:49:24,882][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:49:24,883][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:49:24,889][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:49:24,892][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:49:24,892][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:49:25,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:25,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:26,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:27,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:28,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:29,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:29,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:30,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:31,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:32,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:33,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:33,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:34,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:35,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:36,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:37,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:37,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:38,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:39,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:40,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:41,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:41,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:43,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:43,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:44,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:45,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:46,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:47,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:47,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:48,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:49,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:50,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:49:51,828][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:49:52,837][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:49:52,840][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:49:54,176][__main__][INFO] - Iteration 557 took 54s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 33m 36s. Estimated total time: 15h 13m 0s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 18s, 500 more iterations: 7h 36m 30s. +[2025-08-20 16:49:54,178][__main__][INFO] - Starting iteration 557. +[2025-08-20 16:50:17,205][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:50:17,207][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:50:17,213][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:50:19,663][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:50:19,664][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:50:19,671][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:50:19,673][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:50:19,674][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:50:19,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:20,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:21,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:22,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:23,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:23,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:24,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:25,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:26,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:27,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:27,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:28,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:29,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:30,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:31,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:31,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:32,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:33,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:34,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:35,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:36,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:37,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:37,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:38,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:39,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:40,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:41,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:41,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:42,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:43,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:44,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:45,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:50:46,728][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:50:47,683][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:50:47,685][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:50:49,211][__main__][INFO] - Iteration 558 took 55s (37.40% Gen, 62.60% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 36m 54s. Estimated total time: 15h 17m 12s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 36s. +[2025-08-20 16:50:49,213][__main__][INFO] - Starting iteration 558. +[2025-08-20 16:51:12,654][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:51:12,655][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:51:12,661][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:51:15,106][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:51:15,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:51:15,113][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:51:15,116][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:51:15,116][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:51:15,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:16,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:17,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:17,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:18,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:19,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:20,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:20,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:21,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:22,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:23,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:24,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:24,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:25,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:26,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:27,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:28,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:28,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:29,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:30,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:31,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:32,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:32,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:33,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:34,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:35,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:36,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:37,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:38,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:38,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:39,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:40,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:51:42,084][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:51:43,045][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:51:43,046][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:51:44,381][__main__][INFO] - Iteration 559 took 55s (38.05% Gen, 61.95% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 38m 14s. Estimated total time: 15h 19m 28s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 56s, 500 more iterations: 7h 39m 44s. +[2025-08-20 16:51:44,384][__main__][INFO] - Starting iteration 559. +[2025-08-20 16:52:07,483][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:52:07,484][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:52:07,491][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:52:09,943][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:52:09,945][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:52:09,951][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:52:09,954][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:52:09,954][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:52:10,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:11,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:11,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:12,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:13,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:14,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:15,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:15,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:16,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:17,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:18,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:18,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:19,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:20,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:21,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:22,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:22,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:23,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:24,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:25,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:26,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:26,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:27,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:28,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:29,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:30,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:31,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:32,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:32,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:33,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:34,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:35,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:52:36,894][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:52:37,896][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:52:37,898][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:52:39,531][__main__][INFO] - Iteration 560 took 55s (37.44% Gen, 62.56% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 36m 56s. Estimated total time: 15h 19m 5s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 32s. +[2025-08-20 16:52:39,533][__main__][INFO] - Starting iteration 560. +[2025-08-20 16:53:02,733][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:02,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:02,740][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:05,185][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:05,187][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:05,194][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:05,196][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:53:05,196][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:53:05,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:06,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:07,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:07,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:08,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:09,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:10,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:11,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:11,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:12,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:13,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:14,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:15,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:15,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:16,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:17,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:18,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:18,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:19,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:20,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:21,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:22,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:23,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:24,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:25,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:25,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:26,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:27,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:28,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:29,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:29,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:30,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:53:32,257][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:53:33,304][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:53:33,308][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:53:34,673][__main__][INFO] - Iteration 561 took 55s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 35m 56s. Estimated total time: 15h 18m 59s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 53s, 500 more iterations: 7h 39m 29s. +[2025-08-20 16:53:34,676][__main__][INFO] - Starting iteration 561. +[2025-08-20 16:53:58,332][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:58,333][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:53:58,340][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:00,781][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:00,782][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:00,788][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:00,790][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:54:00,791][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:54:01,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:01,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:02,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:03,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:04,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:05,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:05,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:06,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:07,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:08,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:09,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:09,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:10,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:11,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:12,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:12,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:13,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:14,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:15,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:16,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:16,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:18,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:18,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:19,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:20,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:21,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:22,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:22,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:23,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:24,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:25,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:26,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:27,714][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:54:28,960][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:54:28,963][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:54:30,353][__main__][INFO] - Iteration 562 took 55s (38.05% Gen, 61.94% Train). Generation: 21s, Training: 34s. Estimated remaining time: 6h 43m 54s. Estimated total time: 15h 27m 53s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 47s, 500 more iterations: 7h 43m 56s. +[2025-08-20 16:54:30,355][__main__][INFO] - Starting iteration 562. +[2025-08-20 16:54:53,353][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:53,355][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:53,361][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:55,809][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:55,810][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:55,817][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:54:55,819][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:54:55,819][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:54:56,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:56,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:57,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:58,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:54:59,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:00,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:00,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:01,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:02,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:03,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:04,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:04,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:05,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:06,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:07,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:08,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:08,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:09,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:10,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:11,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:12,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:13,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:14,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:14,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:15,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:16,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:17,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:17,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:18,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:19,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:20,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:21,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:22,748][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:55:23,756][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:55:23,758][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:55:25,648][__main__][INFO] - Iteration 563 took 55s (37.18% Gen, 62.82% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 36m 38s. Estimated total time: 15h 21m 33s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 9s, 500 more iterations: 7h 40m 46s. +[2025-08-20 16:55:25,650][__main__][INFO] - Starting iteration 563. +[2025-08-20 16:55:49,047][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:55:49,048][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:55:49,055][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:55:51,537][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:55:51,538][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:55:51,545][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:55:51,547][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:55:51,547][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:55:51,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:52,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:53,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:54,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:55,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:55,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:56,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:57,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:58,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:58,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:55:59,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:00,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:01,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:02,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:02,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:03,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:04,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:05,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:06,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:06,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:07,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:09,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:09,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:10,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:11,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:12,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:12,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:13,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:14,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:15,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:16,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:16,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:18,544][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:56:19,585][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:56:19,588][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:56:20,918][__main__][INFO] - Iteration 564 took 55s (37.86% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 35m 17s. Estimated total time: 15h 21m 7s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 33s. +[2025-08-20 16:56:20,920][__main__][INFO] - Starting iteration 564. +[2025-08-20 16:56:44,053][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:56:44,055][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:56:44,061][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:56:46,493][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:56:46,494][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:56:46,500][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:56:46,502][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:56:46,503][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:56:46,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:47,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:48,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:49,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:49,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:50,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:51,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:52,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:53,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:53,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:54,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:55,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:56,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:57,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:57,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:58,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:56:59,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:00,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:01,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:02,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:03,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:03,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:04,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:05,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:06,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:07,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:07,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:08,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:09,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:10,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:11,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:11,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:13,448][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:57:14,458][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:57:14,460][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:57:15,820][__main__][INFO] - Iteration 565 took 54s (37.70% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 28m 14s. Estimated total time: 15h 14m 59s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 29s. +[2025-08-20 16:57:15,821][__main__][INFO] - Starting iteration 565. +[2025-08-20 16:57:39,101][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:57:39,103][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:57:39,109][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:57:41,566][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:57:41,567][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:57:41,574][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:57:41,576][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:57:41,576][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:57:41,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:42,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:43,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:44,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:45,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:45,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:46,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:47,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:48,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:49,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:49,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:50,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:51,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:52,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:52,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:53,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:54,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:55,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:56,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:56,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:57,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:58,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:57:59,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:00,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:01,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:02,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:02,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:03,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:04,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:05,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:06,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:06,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:08,518][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:58:09,679][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:58:09,682][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:58:11,152][__main__][INFO] - Iteration 566 took 55s (37.65% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 34m 30s. Estimated total time: 15h 22m 10s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 5s. +[2025-08-20 16:58:11,154][__main__][INFO] - Starting iteration 566. +[2025-08-20 16:58:34,389][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:58:34,391][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:58:34,397][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:58:36,874][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:58:36,875][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:58:36,882][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:58:36,884][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:58:36,885][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:58:37,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:37,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:38,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:39,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:40,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:41,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:41,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:42,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:43,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:44,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:45,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:45,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:46,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:47,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:48,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:49,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:49,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:51,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:51,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:52,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:53,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:54,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:55,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:55,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:56,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:57,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:58,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:59,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:58:59,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:00,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:01,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:02,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:03,882][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 16:59:04,845][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 16:59:04,847][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 16:59:06,133][__main__][INFO] - Iteration 567 took 54s (37.78% Gen, 62.22% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 27m 43s. Estimated total time: 15h 16m 19s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 9s. +[2025-08-20 16:59:06,135][__main__][INFO] - Starting iteration 567. +[2025-08-20 16:59:29,628][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:59:29,629][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:59:29,635][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:59:32,075][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:59:32,077][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:59:32,083][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 16:59:32,085][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 16:59:32,085][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 16:59:32,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:33,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:33,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:34,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:35,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:36,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:37,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:37,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:38,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:39,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:40,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:41,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:41,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:42,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:43,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:44,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:45,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:45,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:46,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:47,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:48,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:49,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:49,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:50,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:51,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:52,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:53,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:54,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:55,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:55,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:56,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:57,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 16:59:59,107][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:00:00,084][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:00:00,086][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:00:01,494][__main__][INFO] - Iteration 568 took 55s (38.01% Gen, 61.99% Train). Generation: 21s, Training: 34s. Estimated remaining time: 6h 33m 7s. Estimated total time: 15h 22m 38s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 15s, 500 more iterations: 7h 41m 19s. +[2025-08-20 17:00:01,495][__main__][INFO] - Starting iteration 568. +[2025-08-20 17:00:25,015][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:00:25,016][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:00:25,023][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:00:27,465][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:00:28,045][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:00:28,802][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:00:28,807][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:00:28,808][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:00:29,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:29,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:30,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:31,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:32,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:33,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:33,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:34,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:35,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:36,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:37,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:37,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:38,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:39,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:40,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:41,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:41,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:42,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:43,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:44,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:45,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:46,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:47,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:47,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:48,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:49,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:50,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:50,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:51,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:52,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:53,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:54,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:00:55,761][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:00:56,706][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:00:56,708][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:00:58,136][__main__][INFO] - Iteration 569 took 56s (37.22% Gen, 62.78% Train). Generation: 21s, Training: 35s. Estimated remaining time: 6h 53m 33s. Estimated total time: 15h 44m 0s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 24s, 500 more iterations: 7h 52m 0s. +[2025-08-20 17:00:58,137][__main__][INFO] - Starting iteration 569. +[2025-08-20 17:01:21,305][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:01:21,307][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:01:21,313][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:01:23,753][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:01:23,755][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:01:23,761][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:01:23,763][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:01:23,764][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:01:24,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:24,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:25,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:26,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:27,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:28,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:28,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:29,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:30,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:31,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:32,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:32,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:33,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:34,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:35,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:35,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:36,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:37,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:38,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:39,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:39,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:40,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:42,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:42,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:43,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:44,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:45,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:46,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:46,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:47,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:48,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:49,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:01:50,814][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:01:51,811][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:01:51,812][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:01:53,139][__main__][INFO] - Iteration 570 took 55s (37.71% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 25m 19s. Estimated total time: 15h 16m 41s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 20s. +[2025-08-20 17:01:53,141][__main__][INFO] - Starting iteration 570. +[2025-08-20 17:02:16,259][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:02:16,260][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:02:16,266][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:02:18,723][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:02:18,725][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:02:18,731][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:02:18,733][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:02:18,734][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:02:19,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:19,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:20,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:21,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:22,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:22,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:23,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:24,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:25,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:26,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:26,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:27,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:28,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:29,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:30,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:30,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:31,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:32,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:33,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:34,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:34,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:36,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:36,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:37,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:38,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:39,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:40,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:40,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:41,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:42,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:43,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:44,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:02:45,664][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:02:46,630][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:02:46,632][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:02:48,211][__main__][INFO] - Iteration 571 took 55s (37.51% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 25m 32s. Estimated total time: 15h 17m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 54s. +[2025-08-20 17:02:48,212][__main__][INFO] - Starting iteration 571. +[2025-08-20 17:03:11,806][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:03:11,807][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:03:11,813][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:03:14,254][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:03:14,256][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:03:14,262][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:03:14,264][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:03:14,265][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:03:14,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:15,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:16,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:16,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:17,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:18,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:19,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:20,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:20,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:21,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:22,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:23,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:24,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:24,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:25,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:26,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:27,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:28,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:29,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:30,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:30,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:31,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:32,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:33,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:34,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:34,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:35,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:36,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:37,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:38,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:38,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:39,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:03:41,242][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:03:42,185][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:03:42,187][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:03:43,555][__main__][INFO] - Iteration 572 took 55s (38.19% Gen, 61.81% Train). Generation: 21s, Training: 34s. Estimated remaining time: 6h 29m 9s. Estimated total time: 15h 22m 21s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 10s. +[2025-08-20 17:03:43,556][__main__][INFO] - Starting iteration 572. +[2025-08-20 17:04:06,745][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:04:06,747][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:04:06,753][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:04:09,245][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:04:09,247][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:04:09,253][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:04:09,255][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:04:09,256][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:04:09,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:10,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:11,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:11,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:12,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:13,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:14,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:15,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:15,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:16,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:17,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:18,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:19,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:19,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:20,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:21,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:22,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:23,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:23,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:24,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:25,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:26,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:27,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:28,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:29,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:29,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:30,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:31,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:32,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:33,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:33,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:34,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:04:36,311][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:04:37,286][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:04:37,288][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:04:38,756][__main__][INFO] - Iteration 573 took 55s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 25m 50s. Estimated total time: 15h 19m 58s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 59s, 500 more iterations: 7h 39m 59s. +[2025-08-20 17:04:38,757][__main__][INFO] - Starting iteration 573. +[2025-08-20 17:05:02,087][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:02,088][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:02,094][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:04,540][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:04,541][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:04,547][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:04,550][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:05:04,550][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:05:04,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:05,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:06,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:07,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:08,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:08,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:09,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:10,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:11,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:11,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:12,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:13,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:14,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:15,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:15,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:16,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:17,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:18,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:19,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:19,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:21,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:22,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:22,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:23,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:24,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:25,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:25,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:26,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:27,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:28,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:29,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:29,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:05:31,572][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:05:32,907][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:05:32,910][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:05:34,862][__main__][INFO] - Iteration 574 took 56s (37.25% Gen, 62.75% Train). Generation: 20s, Training: 35s. Estimated remaining time: 6h 40m 0s. Estimated total time: 15h 35m 4s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 30s, 500 more iterations: 7h 47m 32s. +[2025-08-20 17:05:34,864][__main__][INFO] - Starting iteration 574. +[2025-08-20 17:05:57,872][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:57,873][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:05:57,880][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:00,327][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:00,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:00,335][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:00,337][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:06:00,338][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:06:00,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:01,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:02,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:03,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:03,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:04,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:05,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:06,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:06,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:07,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:08,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:09,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:10,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:10,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:11,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:12,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:13,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:14,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:15,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:16,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:17,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:18,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:18,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:19,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:20,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:21,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:22,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:22,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:23,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:24,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:25,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:26,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:27,620][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:06:28,568][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:06:28,570][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:06:30,056][__main__][INFO] - Iteration 575 took 55s (37.27% Gen, 62.73% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 23m 52s. Estimated total time: 15h 19m 51s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 59s, 500 more iterations: 7h 39m 55s. +[2025-08-20 17:06:30,058][__main__][INFO] - Starting iteration 575. +[2025-08-20 17:06:53,103][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:53,104][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:53,111][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:55,550][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:55,551][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:55,557][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:06:55,560][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:06:55,560][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:06:55,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:56,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:57,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:58,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:59,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:06:59,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:00,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:01,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:02,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:02,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:03,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:04,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:05,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:06,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:06,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:07,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:08,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:09,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:10,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:10,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:11,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:12,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:13,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:14,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:15,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:16,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:16,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:17,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:18,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:19,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:20,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:20,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:22,580][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:07:23,625][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:07:23,627][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:07:24,936][__main__][INFO] - Iteration 576 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 17m 43s. Estimated total time: 15h 14m 37s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 18s. +[2025-08-20 17:07:24,937][__main__][INFO] - Starting iteration 576. +[2025-08-20 17:07:48,052][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:07:48,054][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:07:48,061][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:07:50,510][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:07:50,511][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:07:50,517][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:07:50,519][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:07:50,520][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:07:50,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:51,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:52,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:53,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:53,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:54,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:55,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:56,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:57,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:57,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:58,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:07:59,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:00,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:01,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:01,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:02,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:03,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:04,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:05,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:05,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:06,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:07,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:08,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:09,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:10,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:11,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:11,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:12,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:13,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:14,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:15,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:15,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:17,446][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:08:18,406][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:08:18,408][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:08:19,952][__main__][INFO] - Iteration 577 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 19m 6s. Estimated total time: 15h 16m 55s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 27s. +[2025-08-20 17:08:19,954][__main__][INFO] - Starting iteration 577. +[2025-08-20 17:08:43,123][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:08:43,124][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:08:43,130][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:08:45,587][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:08:45,588][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:08:45,594][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:08:45,596][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:08:45,597][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:08:45,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:46,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:47,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:48,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:49,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:49,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:50,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:51,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:52,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:53,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:53,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:54,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:55,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:56,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:57,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:57,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:58,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:08:59,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:00,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:00,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:01,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:02,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:03,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:04,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:05,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:06,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:07,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:07,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:08,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:09,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:10,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:11,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:12,643][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:09:13,690][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:09:13,692][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:09:14,961][__main__][INFO] - Iteration 578 took 55s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 18m 3s. Estimated total time: 15h 16m 47s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 23s. +[2025-08-20 17:09:14,963][__main__][INFO] - Starting iteration 578. +[2025-08-20 17:09:38,002][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:09:38,004][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:09:38,010][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:09:40,432][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:09:40,433][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:09:40,439][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:09:40,441][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:09:40,442][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:09:40,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:41,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:42,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:43,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:43,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:44,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:45,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:46,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:47,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:47,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:48,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:49,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:50,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:51,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:51,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:52,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:53,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:54,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:54,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:55,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:56,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:57,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:58,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:09:59,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:00,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:00,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:01,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:02,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:03,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:04,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:04,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:05,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:07,332][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:10:08,284][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:10:08,286][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:10:09,901][__main__][INFO] - Iteration 579 took 54s (37.51% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 15m 58s. Estimated total time: 15h 15m 37s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 48s. +[2025-08-20 17:10:09,903][__main__][INFO] - Starting iteration 579. +[2025-08-20 17:10:33,398][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:10:33,400][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:10:33,406][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:10:35,854][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:10:35,855][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:10:35,861][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:10:35,863][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:10:35,864][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:10:36,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:36,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:37,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:38,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:39,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:40,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:40,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:41,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:42,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:43,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:44,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:44,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:45,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:46,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:47,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:48,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:48,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:49,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:50,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:51,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:52,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:53,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:54,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:54,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:55,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:56,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:57,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:58,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:58,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:10:59,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:00,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:01,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:02,823][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:11:03,782][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:11:03,784][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:11:05,212][__main__][INFO] - Iteration 580 took 55s (38.03% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 6h 21m 14s. Estimated total time: 15h 21m 49s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 10s, 500 more iterations: 7h 40m 54s. +[2025-08-20 17:11:05,214][__main__][INFO] - Starting iteration 580. +[2025-08-20 17:11:28,232][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:11:28,233][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:11:28,239][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:11:30,671][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:11:30,672][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:11:30,679][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:11:30,681][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:11:30,681][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:11:30,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:31,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:32,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:33,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:34,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:34,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:35,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:36,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:37,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:38,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:38,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:39,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:40,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:41,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:42,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:42,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:43,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:44,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:45,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:46,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:46,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:47,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:48,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:49,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:50,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:51,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:52,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:52,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:53,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:54,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:55,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:56,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:11:57,734][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:11:58,716][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:11:58,717][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:12:00,120][__main__][INFO] - Iteration 581 took 54s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 13m 36s. Estimated total time: 15h 15m 5s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 32s. +[2025-08-20 17:12:00,122][__main__][INFO] - Starting iteration 581. +[2025-08-20 17:12:23,271][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:12:23,272][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:12:23,279][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:12:25,726][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:12:25,727][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:12:25,734][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:12:25,736][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:12:25,737][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:12:26,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:26,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:27,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:28,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:29,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:30,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:30,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:31,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:32,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:33,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:34,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:35,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:35,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:36,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:37,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:38,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:38,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:39,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:40,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:41,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:42,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:43,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:44,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:45,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:45,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:46,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:47,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:48,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:48,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:49,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:50,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:51,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:12:53,000][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:12:53,949][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:12:53,950][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:12:55,399][__main__][INFO] - Iteration 582 took 55s (37.45% Gen, 62.54% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 18m 52s. Estimated total time: 15h 21m 16s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 7s, 500 more iterations: 7h 40m 38s. +[2025-08-20 17:12:55,400][__main__][INFO] - Starting iteration 582. +[2025-08-20 17:13:18,514][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:13:18,515][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:13:18,522][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:13:20,966][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:13:20,968][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:13:20,974][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:13:20,976][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:13:20,976][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:13:21,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:22,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:22,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:23,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:24,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:25,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:26,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:26,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:27,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:28,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:29,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:29,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:30,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:31,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:32,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:33,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:33,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:34,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:35,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:36,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:37,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:37,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:39,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:40,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:40,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:41,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:42,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:43,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:43,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:44,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:45,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:46,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:13:47,972][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:13:49,147][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:13:49,149][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:13:50,874][__main__][INFO] - Iteration 583 took 55s (37.26% Gen, 62.74% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 21m 13s. Estimated total time: 15h 24m 33s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 27s, 500 more iterations: 7h 42m 16s. +[2025-08-20 17:13:50,875][__main__][INFO] - Starting iteration 583. +[2025-08-20 17:14:14,031][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:14:14,032][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:14:14,039][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:14:16,499][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:14:16,500][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:14:16,507][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:14:16,509][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:14:16,509][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:14:16,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:17,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:18,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:19,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:19,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:20,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:21,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:22,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:23,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:23,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:24,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:25,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:26,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:27,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:27,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:28,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:29,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:30,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:31,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:32,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:33,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:33,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:34,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:35,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:36,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:37,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:37,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:38,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:39,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:40,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:41,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:41,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:14:43,604][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:14:44,767][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:14:44,770][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:14:46,142][__main__][INFO] - Iteration 584 took 55s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 16m 51s. Estimated total time: 15h 21m 6s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 33s. +[2025-08-20 17:14:46,144][__main__][INFO] - Starting iteration 584. +[2025-08-20 17:15:09,287][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:15:09,288][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:15:09,295][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:15:11,744][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:15:11,745][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:15:11,751][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:15:11,754][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:15:11,754][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:15:12,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:12,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:13,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:14,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:15,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:16,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:16,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:17,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:18,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:19,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:19,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:20,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:21,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:22,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:23,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:23,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:24,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:25,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:26,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:27,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:27,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:29,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:29,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:30,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:31,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:32,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:33,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:33,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:34,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:35,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:36,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:37,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:15:38,684][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:15:39,668][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:15:39,670][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:15:41,363][__main__][INFO] - Iteration 585 took 55s (37.48% Gen, 62.51% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 15m 8s. Estimated total time: 15h 20m 18s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 1s, 500 more iterations: 7h 40m 9s. +[2025-08-20 17:15:41,365][__main__][INFO] - Starting iteration 585. +[2025-08-20 17:16:04,794][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:04,796][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:04,802][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:07,261][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:07,262][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:07,269][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:07,271][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:16:07,271][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:16:07,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:08,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:09,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:09,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:10,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:11,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:12,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:13,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:13,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:14,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:15,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:16,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:17,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:17,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:18,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:19,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:20,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:21,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:21,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:22,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:23,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:24,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:25,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:26,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:27,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:27,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:28,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:29,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:30,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:31,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:31,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:32,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:16:34,239][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:16:35,175][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:16:35,177][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:16:36,782][__main__][INFO] - Iteration 586 took 55s (37.86% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 17m 31s. Estimated total time: 15h 23m 37s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 21s, 500 more iterations: 7h 41m 48s. +[2025-08-20 17:16:36,784][__main__][INFO] - Starting iteration 586. +[2025-08-20 17:16:59,879][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:59,880][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:16:59,886][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:02,349][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:02,350][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:02,357][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:02,359][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:17:02,360][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:17:02,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:03,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:04,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:05,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:05,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:06,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:07,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:08,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:09,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:09,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:10,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:11,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:12,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:12,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:13,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:14,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:15,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:16,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:16,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:18,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:19,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:19,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:20,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:21,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:22,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:22,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:23,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:24,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:25,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:26,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:26,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:27,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:29,421][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:17:30,674][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:17:30,676][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:17:31,947][__main__][INFO] - Iteration 587 took 55s (37.43% Gen, 62.57% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 12m 21s. Estimated total time: 15h 19m 22s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 56s, 500 more iterations: 7h 39m 41s. +[2025-08-20 17:17:31,948][__main__][INFO] - Starting iteration 587. +[2025-08-20 17:17:54,985][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:54,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:54,993][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:57,436][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:57,437][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:57,444][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:17:57,446][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:17:57,447][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:17:57,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:58,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:17:59,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:00,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:00,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:01,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:02,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:03,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:04,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:04,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:05,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:06,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:07,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:08,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:08,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:09,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:10,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:11,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:12,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:12,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:13,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:14,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:15,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:16,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:17,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:18,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:18,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:19,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:20,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:21,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:22,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:22,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:24,425][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:18:25,408][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:18:25,410][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:18:26,770][__main__][INFO] - Iteration 588 took 54s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 5m 45s. Estimated total time: 15h 13m 41s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 50s. +[2025-08-20 17:18:26,771][__main__][INFO] - Starting iteration 588. +[2025-08-20 17:18:49,813][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:18:49,814][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:18:49,820][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:18:52,277][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:18:52,278][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:18:52,285][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:18:52,287][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:18:52,287][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:18:52,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:53,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:54,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:54,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:55,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:56,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:57,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:58,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:58,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:18:59,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:00,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:01,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:02,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:02,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:03,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:04,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:05,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:06,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:06,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:07,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:08,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:09,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:10,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:11,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:12,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:12,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:13,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:14,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:15,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:16,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:16,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:17,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:19,323][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:19:20,262][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:19:20,264][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:19:21,939][__main__][INFO] - Iteration 589 took 55s (37.31% Gen, 62.69% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 10m 36s. Estimated total time: 15h 19m 27s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 56s, 500 more iterations: 7h 39m 43s. +[2025-08-20 17:19:21,941][__main__][INFO] - Starting iteration 589. +[2025-08-20 17:19:44,890][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:19:44,891][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:19:44,898][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:19:47,325][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:19:47,326][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:19:47,332][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:19:47,335][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:19:47,335][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:19:47,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:48,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:49,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:50,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:50,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:51,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:52,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:53,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:53,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:54,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:55,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:56,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:57,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:57,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:58,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:19:59,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:00,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:01,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:01,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:02,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:03,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:04,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:05,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:06,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:07,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:07,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:08,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:09,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:10,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:11,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:11,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:12,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:14,395][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:20:15,341][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:20:15,342][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:20:16,755][__main__][INFO] - Iteration 590 took 54s (37.44% Gen, 62.56% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 3m 48s. Estimated total time: 15h 13m 33s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 46s. +[2025-08-20 17:20:16,757][__main__][INFO] - Starting iteration 590. +[2025-08-20 17:20:40,214][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:20:40,215][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:20:40,221][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:20:42,671][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:20:42,673][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:20:42,679][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:20:42,681][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:20:42,682][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:20:42,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:43,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:44,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:45,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:46,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:46,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:47,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:48,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:49,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:50,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:50,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:51,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:52,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:53,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:54,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:54,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:55,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:56,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:57,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:58,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:20:58,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:00,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:00,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:01,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:02,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:03,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:04,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:04,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:05,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:06,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:07,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:08,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:09,655][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:21:10,594][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:21:10,595][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:21:12,840][__main__][INFO] - Iteration 591 took 56s (37.46% Gen, 62.54% Train). Generation: 21s, Training: 35s. Estimated remaining time: 6h 24m 1s. Estimated total time: 15h 34m 43s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 28s, 500 more iterations: 7h 47m 21s. +[2025-08-20 17:21:12,842][__main__][INFO] - Starting iteration 591. +[2025-08-20 17:21:35,862][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:21:35,864][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:21:35,870][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:21:38,336][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:21:38,338][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:21:38,344][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:21:38,346][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:21:38,347][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:21:38,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:39,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:40,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:41,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:41,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:42,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:43,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:44,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:44,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:45,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:46,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:47,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:48,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:48,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:49,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:50,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:51,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:52,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:52,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:53,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:54,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:55,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:56,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:57,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:58,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:58,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:21:59,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:00,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:01,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:02,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:02,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:03,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:05,374][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:22:06,510][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:22:06,512][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:22:07,903][__main__][INFO] - Iteration 592 took 55s (37.33% Gen, 62.67% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 6m 4s. Estimated total time: 15h 17m 41s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 50s. +[2025-08-20 17:22:07,905][__main__][INFO] - Starting iteration 592. +[2025-08-20 17:22:30,800][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:22:30,801][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:22:30,807][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:22:33,245][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:22:33,246][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:22:33,252][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:22:33,254][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:22:33,255][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:22:33,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:34,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:35,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:35,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:36,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:37,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:38,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:39,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:39,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:40,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:41,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:42,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:43,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:43,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:44,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:45,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:46,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:47,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:47,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:48,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:49,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:50,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:51,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:52,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:53,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:53,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:54,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:55,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:56,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:57,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:57,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:22:58,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:00,294][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:23:01,239][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:23:01,240][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:23:07,786][__main__][INFO] - Iteration 593 took 59s (34.16% Gen, 65.84% Train). Generation: 20s, Training: 39s. Estimated remaining time: 7h 25m 20s. Estimated total time: 16h 37m 56s. Time estimates for 10 more iterations: 9m 58s, 100 more iterations: 1h 39m 47s, 500 more iterations: 8h 18m 58s. +[2025-08-20 17:23:07,787][__main__][INFO] - Starting iteration 593. +[2025-08-20 17:23:30,801][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:23:30,803][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:23:30,809][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:23:33,250][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:23:33,251][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:23:33,257][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:23:33,260][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:23:33,260][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:23:33,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:34,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:35,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:35,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:36,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:37,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:38,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:39,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:39,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:40,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:41,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:42,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:43,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:43,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:44,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:45,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:46,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:47,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:47,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:48,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:49,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:50,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:51,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:52,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:53,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:53,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:54,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:55,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:56,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:57,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:57,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:23:58,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:00,226][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:24:01,257][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:24:01,259][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:24:02,643][__main__][INFO] - Iteration 594 took 54s (37.50% Gen, 62.50% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 0m 43s. Estimated total time: 15h 14m 15s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 25s, 500 more iterations: 7h 37m 7s. +[2025-08-20 17:24:02,645][__main__][INFO] - Starting iteration 594. +[2025-08-20 17:24:25,686][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:24:25,687][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:24:25,694][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:24:28,168][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:24:28,170][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:24:28,176][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:24:28,178][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:24:28,179][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:24:28,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:29,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:30,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:30,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:31,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:32,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:33,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:34,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:34,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:35,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:36,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:37,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:37,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:38,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:39,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:40,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:41,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:42,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:43,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:44,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:44,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:45,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:46,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:47,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:48,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:48,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:49,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:50,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:51,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:51,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:52,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:53,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:24:55,256][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:24:56,228][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:24:56,229][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:24:57,518][__main__][INFO] - Iteration 595 took 54s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 0m 5s. Estimated total time: 15h 14m 32s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 16s. +[2025-08-20 17:24:57,519][__main__][INFO] - Starting iteration 595. +[2025-08-20 17:25:20,801][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:25:20,802][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:25:20,808][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:25:23,257][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:25:23,258][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:25:23,264][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:25:23,267][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:25:23,267][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:25:23,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:24,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:25,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:25,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:26,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:27,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:28,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:29,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:29,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:30,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:31,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:32,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:33,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:33,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:34,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:35,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:36,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:37,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:37,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:39,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:39,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:40,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:41,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:42,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:43,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:43,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:44,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:45,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:46,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:47,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:47,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:48,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:25:50,256][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:25:51,466][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:25:51,468][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:25:52,891][__main__][INFO] - Iteration 596 took 55s (37.64% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 7m 29s. Estimated total time: 15h 22m 51s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 17s, 500 more iterations: 7h 41m 25s. +[2025-08-20 17:25:52,893][__main__][INFO] - Starting iteration 596. +[2025-08-20 17:26:15,892][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:26:15,893][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:26:15,900][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:26:18,349][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:26:18,351][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:26:18,357][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:26:18,359][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:26:18,360][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:26:18,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:19,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:20,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:21,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:21,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:22,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:23,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:24,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:25,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:25,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:26,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:27,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:28,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:28,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:29,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:30,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:31,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:32,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:32,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:34,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:35,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:35,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:36,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:37,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:38,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:39,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:39,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:40,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:41,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:42,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:43,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:43,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:26:45,433][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:26:46,379][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:26:46,380][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:26:47,844][__main__][INFO] - Iteration 597 took 54s (37.41% Gen, 62.59% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 59m 34s. Estimated total time: 15h 15m 50s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 55s. +[2025-08-20 17:26:47,846][__main__][INFO] - Starting iteration 597. +[2025-08-20 17:27:10,780][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:27:10,782][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:27:10,788][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:27:13,276][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:27:13,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:27:13,283][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:27:13,285][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:27:13,286][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:27:13,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:14,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:15,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:15,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:16,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:17,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:18,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:19,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:19,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:20,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:21,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:22,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:23,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:23,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:24,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:25,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:26,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:27,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:27,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:28,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:29,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:30,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:31,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:32,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:33,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:33,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:34,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:35,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:36,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:37,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:37,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:38,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:27:40,381][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:27:41,367][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:27:41,368][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:27:42,713][__main__][INFO] - Iteration 598 took 54s (37.29% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 57m 15s. Estimated total time: 15h 14m 27s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 13s. +[2025-08-20 17:27:42,715][__main__][INFO] - Starting iteration 598. +[2025-08-20 17:28:05,639][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:28:05,640][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:28:05,646][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:28:08,084][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:28:08,085][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:28:08,091][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:28:08,093][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:28:08,094][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:28:08,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:09,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:09,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:10,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:11,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:12,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:13,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:13,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:14,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:17,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:18,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:18,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:19,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:20,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:21,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:21,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:22,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:23,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:24,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:25,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:25,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:26,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:28,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:28,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:29,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:30,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:31,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:31,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:32,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:33,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:34,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:35,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:28:36,817][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:28:37,789][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:28:37,790][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:28:39,253][__main__][INFO] - Iteration 599 took 56s (36.23% Gen, 63.77% Train). Generation: 20s, Training: 36s. Estimated remaining time: 6h 24m 9s. Estimated total time: 15h 42m 17s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 13s, 500 more iterations: 7h 51m 8s. +[2025-08-20 17:28:39,254][__main__][INFO] - Starting iteration 599. +[2025-08-20 17:29:02,398][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:02,399][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:02,405][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:04,868][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:04,869][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:04,876][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:04,878][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:29:04,878][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:29:05,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:05,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:06,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:07,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:08,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:09,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:09,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:10,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:11,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:12,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:13,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:13,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:14,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:15,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:16,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:17,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:17,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:18,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:19,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:20,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:21,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:22,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:23,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:23,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:24,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:25,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:26,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:27,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:27,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:28,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:29,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:30,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:29:31,907][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:29:32,852][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:29:32,853][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:29:34,209][__main__][INFO] - Iteration 600 took 54s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 56m 50s. Estimated total time: 15h 15m 54s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 57s. +[2025-08-20 17:29:34,210][__main__][INFO] - Starting iteration 600. +[2025-08-20 17:29:57,234][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:57,235][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:57,241][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:59,690][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:59,691][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:59,697][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:29:59,700][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:29:59,700][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:29:59,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:00,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:01,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:02,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:03,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:03,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:04,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:05,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:06,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:07,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:07,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:08,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:09,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:10,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:11,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:11,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:12,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:13,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:14,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:15,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:16,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:17,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:17,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:18,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:19,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:20,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:21,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:21,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:22,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:23,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:24,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:25,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:26,773][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:30:27,696][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:30:27,697][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:30:32,176][__main__][INFO] - Iteration 601 took 57s (35.47% Gen, 59.68% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 46m 4s. Estimated total time: 16h 6m 5s. Time estimates for 10 more iterations: 9m 39s, 100 more iterations: 1h 36m 36s, 500 more iterations: 8h 3m 2s. +[2025-08-20 17:30:32,177][__main__][INFO] - Starting iteration 601. +[2025-08-20 17:30:55,397][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:30:55,399][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:30:55,405][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:30:57,856][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:30:57,857][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:30:57,864][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:30:57,866][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:30:57,866][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:30:58,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:58,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:30:59,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:00,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:01,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:02,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:02,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:03,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:04,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:05,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:06,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:06,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:07,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:08,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:09,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:10,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:10,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:11,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:12,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:13,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:14,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:15,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:16,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:16,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:17,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:18,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:19,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:20,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:20,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:21,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:22,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:23,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:24,878][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:31:25,838][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:31:25,840][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:31:29,499][__main__][INFO] - Iteration 602 took 57s (36.24% Gen, 63.75% Train). Generation: 20s, Training: 36s. Estimated remaining time: 6h 34m 22s. Estimated total time: 15h 55m 21s. Time estimates for 10 more iterations: 9m 33s, 100 more iterations: 1h 35m 32s, 500 more iterations: 7h 57m 40s. +[2025-08-20 17:31:29,501][__main__][INFO] - Starting iteration 602. +[2025-08-20 17:31:52,380][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:31:52,382][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:31:52,388][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:31:54,837][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:31:54,838][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:31:54,845][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:31:54,847][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:31:54,847][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:31:55,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:55,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:56,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:57,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:58,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:59,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:31:59,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:00,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:01,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:02,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:03,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:03,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:04,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:05,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:06,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:07,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:07,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:08,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:09,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:10,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:11,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:11,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:13,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:13,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:14,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:15,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:16,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:17,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:17,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:18,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:19,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:20,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:21,879][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:32:22,839][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:32:22,840][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:32:24,175][__main__][INFO] - Iteration 603 took 54s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 49m 20s. Estimated total time: 15h 11m 13s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 7s, 500 more iterations: 7h 35m 36s. +[2025-08-20 17:32:24,176][__main__][INFO] - Starting iteration 603. +[2025-08-20 17:32:47,137][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:32:47,138][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:32:47,144][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:32:49,569][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:32:49,570][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:32:49,577][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:32:49,579][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:32:49,579][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:32:49,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:50,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:51,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:52,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:53,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:53,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:54,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:55,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:56,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:57,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:57,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:58,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:32:59,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:00,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:00,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:01,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:02,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:03,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:04,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:04,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:05,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:06,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:07,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:08,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:09,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:10,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:11,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:11,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:12,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:13,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:14,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:15,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:16,678][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:33:17,635][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:33:17,637][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:33:18,878][__main__][INFO] - Iteration 604 took 54s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 48m 53s. Estimated total time: 15h 11m 41s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 10s, 500 more iterations: 7h 35m 50s. +[2025-08-20 17:33:18,879][__main__][INFO] - Starting iteration 604. +[2025-08-20 17:33:41,910][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:33:41,912][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:33:41,918][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:33:44,348][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:33:44,349][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:33:44,355][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:33:44,358][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:33:44,358][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:33:44,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:45,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:46,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:47,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:47,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:48,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:49,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:50,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:51,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:51,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:52,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:53,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:54,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:54,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:55,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:56,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:57,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:58,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:33:59,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:00,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:01,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:01,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:02,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:03,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:04,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:05,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:05,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:06,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:07,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:08,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:09,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:09,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:11,415][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:34:12,370][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:34:12,372][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:34:13,750][__main__][INFO] - Iteration 605 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 50m 47s. Estimated total time: 15h 14m 30s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 15s. +[2025-08-20 17:34:13,752][__main__][INFO] - Starting iteration 605. +[2025-08-20 17:34:36,645][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:34:36,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:34:36,653][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:34:39,148][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:34:39,149][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:34:39,155][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:34:39,158][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:34:39,158][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:34:39,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:40,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:41,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:41,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:42,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:43,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:44,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:45,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:45,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:46,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:47,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:48,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:48,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:49,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:50,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:51,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:52,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:53,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:54,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:55,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:55,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:56,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:57,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:58,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:59,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:34:59,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:00,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:01,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:02,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:03,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:03,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:04,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:06,244][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:35:07,221][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:35:07,222][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:35:08,713][__main__][INFO] - Iteration 606 took 54s (37.12% Gen, 62.88% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 51m 23s. Estimated total time: 15h 16m 0s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 0s. +[2025-08-20 17:35:08,714][__main__][INFO] - Starting iteration 606. +[2025-08-20 17:35:31,955][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:35:31,956][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:35:31,963][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:35:34,462][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:35:34,464][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:35:34,470][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:35:34,472][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:35:34,473][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:35:34,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:35,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:36,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:37,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:37,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:38,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:39,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:40,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:41,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:41,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:42,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:43,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:44,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:45,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:45,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:46,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:47,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:48,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:49,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:49,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:51,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:51,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:52,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:53,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:54,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:55,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:55,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:56,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:57,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:58,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:59,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:35:59,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:01,507][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:36:02,496][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:36:02,497][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:36:04,407][__main__][INFO] - Iteration 607 took 55s (37.29% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 6h 2m 38s. Estimated total time: 15h 28m 11s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 49s, 500 more iterations: 7h 44m 5s. +[2025-08-20 17:36:04,408][__main__][INFO] - Starting iteration 607. +[2025-08-20 17:36:27,329][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:36:27,330][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:36:27,337][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:36:29,785][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:36:29,787][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:36:29,793][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:36:29,795][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:36:29,796][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:36:30,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:30,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:31,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:32,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:33,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:34,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:34,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:35,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:36,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:37,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:38,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:38,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:39,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:40,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:41,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:42,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:43,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:44,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:44,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:45,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:46,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:47,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:48,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:48,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:49,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:50,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:51,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:51,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:52,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:53,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:54,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:55,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:36:56,738][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:36:57,686][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:36:57,688][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:36:59,438][__main__][INFO] - Iteration 608 took 55s (37.21% Gen, 62.78% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 50m 41s. Estimated total time: 15h 17m 9s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 34s. +[2025-08-20 17:36:59,440][__main__][INFO] - Starting iteration 608. +[2025-08-20 17:37:22,414][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:37:22,415][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:37:22,421][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:37:24,869][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:37:24,870][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:37:24,877][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:37:24,879][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:37:24,880][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:37:25,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:25,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:26,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:27,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:28,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:29,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:29,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:30,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:31,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:32,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:33,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:33,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:34,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:35,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:36,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:37,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:37,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:38,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:39,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:40,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:41,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:41,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:42,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:43,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:44,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:45,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:46,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:47,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:47,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:48,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:49,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:50,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:37:51,917][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:37:52,864][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:37:52,866][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:37:54,165][__main__][INFO] - Iteration 609 took 54s (37.49% Gen, 62.51% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 44m 41s. Estimated total time: 15h 12m 4s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 12s, 500 more iterations: 7h 36m 2s. +[2025-08-20 17:37:54,167][__main__][INFO] - Starting iteration 609. +[2025-08-20 17:38:16,973][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:38:16,975][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:38:16,981][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:38:19,430][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:38:19,431][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:38:19,438][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:38:19,441][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:38:19,441][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:38:19,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:20,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:21,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:22,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:22,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:23,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:24,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:25,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:26,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:26,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:27,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:28,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:29,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:30,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:30,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:31,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:32,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:33,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:34,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:35,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:36,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:36,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:37,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:38,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:39,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:40,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:40,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:41,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:42,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:43,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:44,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:44,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:38:46,537][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:38:47,493][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:38:47,494][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:38:48,869][__main__][INFO] - Iteration 610 took 54s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 43m 23s. Estimated total time: 15h 11m 41s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 10s, 500 more iterations: 7h 35m 50s. +[2025-08-20 17:38:48,873][__main__][INFO] - Starting iteration 610. +[2025-08-20 17:39:12,107][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:39:12,109][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:39:12,115][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:39:14,572][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:39:14,573][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:39:14,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:39:14,582][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:39:14,582][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:39:14,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:15,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:16,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:17,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:18,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:18,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:19,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:20,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:21,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:22,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:22,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:23,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:24,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:25,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:25,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:26,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:27,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:28,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:29,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:29,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:31,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:31,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:32,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:33,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:34,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:35,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:35,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:36,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:37,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:38,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:39,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:39,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:39:41,504][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:39:42,452][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:39:42,454][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:39:44,049][__main__][INFO] - Iteration 611 took 55s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 50m 23s. Estimated total time: 15h 19m 36s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 48s. +[2025-08-20 17:39:44,051][__main__][INFO] - Starting iteration 611. +[2025-08-20 17:40:07,482][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:40:07,483][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:40:07,489][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:40:09,960][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:40:09,962][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:40:09,969][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:40:09,971][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:40:09,971][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:40:10,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:11,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:11,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:12,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:13,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:14,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:15,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:15,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:16,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:17,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:18,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:18,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:19,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:20,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:21,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:22,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:22,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:23,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:24,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:25,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:26,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:26,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:27,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:29,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:29,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:30,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:31,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:32,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:33,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:33,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:34,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:35,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:40:36,998][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:40:38,169][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:40:38,171][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:40:39,468][__main__][INFO] - Iteration 612 took 55s (37.85% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 53m 28s. Estimated total time: 15h 23m 37s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 21s, 500 more iterations: 7h 41m 48s. +[2025-08-20 17:40:39,470][__main__][INFO] - Starting iteration 612. +[2025-08-20 17:41:02,332][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:02,333][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:02,339][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:04,804][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:04,805][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:04,812][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:04,814][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:41:04,815][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:41:05,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:05,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:06,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:07,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:08,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:09,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:09,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:10,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:11,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:12,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:13,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:13,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:14,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:15,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:16,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:17,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:17,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:18,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:19,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:20,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:21,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:21,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:23,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:23,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:24,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:25,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:26,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:27,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:27,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:28,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:29,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:30,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:41:31,789][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:41:32,733][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:41:32,734][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:41:34,032][__main__][INFO] - Iteration 613 took 54s (37.37% Gen, 62.63% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 38m 18s. Estimated total time: 15h 9m 21s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 56s, 500 more iterations: 7h 34m 40s. +[2025-08-20 17:41:34,033][__main__][INFO] - Starting iteration 613. +[2025-08-20 17:41:57,090][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:57,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:57,098][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:59,567][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:59,569][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:59,575][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:41:59,577][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:41:59,578][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:41:59,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:00,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:01,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:02,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:03,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:03,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:04,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:05,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:06,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:07,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:07,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:08,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:09,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:10,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:11,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:11,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:12,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:13,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:14,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:15,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:16,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:17,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:17,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:18,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:19,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:20,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:21,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:21,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:22,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:23,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:24,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:25,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:26,625][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:42:27,568][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:42:27,569][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:42:28,855][__main__][INFO] - Iteration 614 took 54s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 41m 43s. Estimated total time: 15h 13m 41s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 50s. +[2025-08-20 17:42:28,857][__main__][INFO] - Starting iteration 614. +[2025-08-20 17:42:51,798][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:42:51,800][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:42:51,806][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:42:54,278][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:42:54,279][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:42:54,285][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:42:54,288][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:42:54,289][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:42:54,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:55,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:56,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:56,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:57,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:58,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:42:59,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:00,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:00,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:01,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:02,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:03,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:04,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:04,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:05,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:06,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:07,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:08,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:08,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:10,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:10,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:11,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:12,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:13,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:14,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:14,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:15,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:16,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:17,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:18,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:18,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:19,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:21,356][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:43:22,408][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:43:22,410][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:43:23,734][__main__][INFO] - Iteration 615 took 54s (37.31% Gen, 62.68% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 41m 43s. Estimated total time: 15h 14m 36s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 18s. +[2025-08-20 17:43:23,735][__main__][INFO] - Starting iteration 615. +[2025-08-20 17:43:46,882][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:43:46,884][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:43:46,890][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:43:49,349][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:43:49,351][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:43:49,357][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:43:49,359][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:43:49,360][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:43:49,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:50,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:51,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:52,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:52,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:53,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:54,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:55,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:56,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:56,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:57,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:58,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:59,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:43:59,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:00,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:01,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:02,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:03,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:03,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:04,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:05,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:06,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:07,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:08,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:09,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:09,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:10,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:11,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:12,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:13,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:13,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:14,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:16,325][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:44:17,284][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:44:17,285][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:44:18,606][__main__][INFO] - Iteration 616 took 54s (37.71% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 40m 42s. Estimated total time: 15h 14m 30s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 15s. +[2025-08-20 17:44:18,607][__main__][INFO] - Starting iteration 616. +[2025-08-20 17:44:41,629][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:44:41,630][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:44:41,636][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:44:44,090][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:44:44,091][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:44:44,098][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:44:44,100][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:44:44,100][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:44:44,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:45,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:45,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:46,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:47,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:48,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:49,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:49,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:50,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:51,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:52,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:53,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:53,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:54,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:55,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:56,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:57,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:57,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:58,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:44:59,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:00,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:01,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:01,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:02,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:04,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:04,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:05,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:06,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:07,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:08,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:08,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:09,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:11,201][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:45:12,158][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:45:12,159][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:45:13,457][__main__][INFO] - Iteration 617 took 54s (37.51% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 39m 26s. Estimated total time: 15h 14m 9s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 4s. +[2025-08-20 17:45:13,458][__main__][INFO] - Starting iteration 617. +[2025-08-20 17:45:36,400][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:45:36,401][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:45:36,407][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:45:38,877][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:45:38,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:45:38,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:45:38,887][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:45:38,888][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:45:39,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:39,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:40,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:41,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:42,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:43,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:43,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:44,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:45,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:46,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:47,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:47,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:48,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:49,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:50,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:51,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:51,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:52,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:53,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:54,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:55,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:56,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:57,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:57,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:58,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:45:59,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:00,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:01,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:01,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:02,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:03,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:04,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:05,873][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:46:06,819][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:46:06,821][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:46:08,172][__main__][INFO] - Iteration 618 took 54s (37.43% Gen, 62.57% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 36m 16s. Estimated total time: 15h 11m 53s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 11s, 500 more iterations: 7h 35m 56s. +[2025-08-20 17:46:08,173][__main__][INFO] - Starting iteration 618. +[2025-08-20 17:46:31,135][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:46:31,136][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:46:31,142][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:46:33,607][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:46:33,608][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:46:33,615][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:46:33,617][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:46:33,618][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:46:33,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:34,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:35,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:36,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:37,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:37,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:38,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:39,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:40,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:41,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:41,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:42,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:43,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:44,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:45,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:45,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:46,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:47,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:48,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:49,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:50,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:51,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:51,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:52,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:53,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:54,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:55,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:55,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:56,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:57,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:58,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:46:59,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:00,641][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:47:01,585][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:47:01,587][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:47:02,811][__main__][INFO] - Iteration 619 took 54s (37.52% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 34m 5s. Estimated total time: 15h 10m 36s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 3s, 500 more iterations: 7h 35m 18s. +[2025-08-20 17:47:02,812][__main__][INFO] - Starting iteration 619. +[2025-08-20 17:47:25,769][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:47:25,770][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:47:25,776][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:47:28,241][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:47:28,243][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:47:28,249][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:47:28,251][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:47:28,252][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:47:28,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:29,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:30,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:30,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:31,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:32,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:33,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:34,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:34,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:35,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:36,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:37,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:38,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:38,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:39,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:40,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:41,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:42,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:43,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:44,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:44,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:45,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:46,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:47,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:48,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:48,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:49,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:50,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:51,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:52,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:52,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:53,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:47:55,332][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:47:56,339][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:47:56,341][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:47:57,616][__main__][INFO] - Iteration 620 took 54s (37.41% Gen, 62.58% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 35m 56s. Estimated total time: 15h 13m 23s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 41s. +[2025-08-20 17:47:57,617][__main__][INFO] - Starting iteration 620. +[2025-08-20 17:48:20,966][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:48:20,968][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:48:20,974][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:48:23,416][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:48:23,417][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:48:23,424][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:48:23,426][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:48:23,427][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:48:23,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:24,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:25,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:26,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:26,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:27,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:28,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:29,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:30,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:30,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:31,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:32,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:33,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:34,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:34,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:35,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:36,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:37,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:38,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:38,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:39,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:40,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:41,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:42,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:43,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:44,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:44,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:45,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:46,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:47,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:48,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:48,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:48:50,371][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:48:51,335][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:48:51,336][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:48:52,739][__main__][INFO] - Iteration 621 took 55s (37.94% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 40m 20s. Estimated total time: 15h 18m 41s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 20s. +[2025-08-20 17:48:52,741][__main__][INFO] - Starting iteration 621. +[2025-08-20 17:49:15,828][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:49:15,829][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:49:15,835][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:49:18,290][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:49:18,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:49:18,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:49:18,301][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:49:18,301][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:49:18,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:19,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:20,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:20,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:21,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:22,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:23,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:24,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:24,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:25,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:26,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:27,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:28,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:28,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:29,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:30,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:31,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:32,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:32,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:33,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:34,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:35,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:36,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:37,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:38,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:39,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:39,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:40,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:41,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:42,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:43,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:43,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:49:45,427][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:49:46,491][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:49:46,493][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:49:47,770][__main__][INFO] - Iteration 622 took 55s (37.49% Gen, 62.51% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 37m 52s. Estimated total time: 15h 17m 8s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 34s. +[2025-08-20 17:49:47,772][__main__][INFO] - Starting iteration 622. +[2025-08-20 17:50:10,816][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:50:10,818][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:50:10,824][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:50:13,287][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:50:13,289][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:50:13,295][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:50:13,298][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:50:13,298][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:50:13,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:14,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:15,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:15,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:16,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:17,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:18,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:19,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:19,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:20,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:21,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:22,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:23,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:23,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:24,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:25,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:26,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:27,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:27,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:28,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:29,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:30,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:31,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:32,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:33,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:33,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:34,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:35,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:36,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:37,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:37,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:38,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:50:40,237][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:50:41,160][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:50:41,162][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:50:42,557][__main__][INFO] - Iteration 623 took 54s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 32m 52s. Estimated total time: 15h 13m 3s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 18s, 500 more iterations: 7h 36m 31s. +[2025-08-20 17:50:42,559][__main__][INFO] - Starting iteration 623. +[2025-08-20 17:51:05,545][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:51:05,547][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:51:05,553][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:51:08,009][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:51:08,011][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:51:08,017][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:51:08,019][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:51:08,020][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:51:08,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:09,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:09,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:10,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:11,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:12,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:13,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:13,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:14,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:15,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:16,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:17,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:17,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:18,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:19,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:20,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:21,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:21,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:22,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:23,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:24,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:25,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:26,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:27,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:27,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:28,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:29,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:30,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:31,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:31,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:32,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:33,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:51:35,072][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:51:36,021][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:51:36,022][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:51:37,420][__main__][INFO] - Iteration 624 took 54s (37.44% Gen, 62.56% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 33m 14s. Estimated total time: 15h 14m 21s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 10s. +[2025-08-20 17:51:37,421][__main__][INFO] - Starting iteration 624. +[2025-08-20 17:52:00,935][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:00,936][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:00,942][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:03,398][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:03,400][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:03,406][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:03,408][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:52:03,409][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:52:03,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:04,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:05,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:06,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:06,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:07,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:08,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:09,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:10,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:10,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:11,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:12,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:13,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:14,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:14,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:15,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:16,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:17,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:18,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:18,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:20,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:20,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:21,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:22,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:23,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:24,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:24,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:25,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:26,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:27,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:28,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:28,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:52:30,418][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:52:31,381][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:52:31,382][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:52:32,792][__main__][INFO] - Iteration 625 took 55s (38.04% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 40m 48s. Estimated total time: 15h 22m 49s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 24s. +[2025-08-20 17:52:32,793][__main__][INFO] - Starting iteration 625. +[2025-08-20 17:52:56,537][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:56,538][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:56,545][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:58,998][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:59,000][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:59,006][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:52:59,008][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:52:59,009][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:52:59,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:00,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:00,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:01,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:02,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:03,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:04,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:04,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:05,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:06,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:07,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:08,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:08,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:09,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:10,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:11,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:12,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:12,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:13,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:14,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:15,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:16,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:17,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:18,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:18,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:19,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:20,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:21,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:21,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:22,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:23,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:24,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:25,933][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:53:26,882][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:53:26,884][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:53:28,161][__main__][INFO] - Iteration 626 took 55s (38.46% Gen, 61.54% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 39m 50s. Estimated total time: 15h 22m 47s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 23s. +[2025-08-20 17:53:28,163][__main__][INFO] - Starting iteration 626. +[2025-08-20 17:53:51,188][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:53:51,190][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:53:51,196][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:53:53,664][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:53:53,665][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:53:53,672][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:53:53,674][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:53:53,675][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:53:53,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:54,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:55,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:56,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:57,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:57,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:58,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:53:59,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:00,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:01,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:01,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:02,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:03,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:04,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:05,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:05,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:06,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:07,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:08,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:09,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:09,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:11,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:11,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:12,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:13,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:14,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:15,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:15,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:16,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:17,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:18,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:19,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:21,424][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:54:22,361][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:54:22,363][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:54:24,981][__main__][INFO] - Iteration 627 took 56s (36.19% Gen, 63.81% Train). Generation: 20s, Training: 36s. Estimated remaining time: 6h 3m 3s. Estimated total time: 15h 46m 57s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 41s, 500 more iterations: 7h 53m 28s. +[2025-08-20 17:54:24,982][__main__][INFO] - Starting iteration 627. +[2025-08-20 17:54:48,066][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:54:48,067][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:54:48,074][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:54:50,517][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:54:50,518][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:54:50,524][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:54:50,527][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:54:50,527][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:54:50,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:51,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:52,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:53,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:54,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:54,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:55,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:56,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:57,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:57,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:58,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:54:59,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:00,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:01,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:01,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:02,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:03,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:04,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:05,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:05,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:07,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:08,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:08,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:09,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:10,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:11,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:12,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:13,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:14,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:15,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:16,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:17,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:18,863][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:55:19,838][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:55:19,840][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:55:21,168][__main__][INFO] - Iteration 628 took 56s (36.72% Gen, 63.27% Train). Generation: 20s, Training: 35s. Estimated remaining time: 5h 51m 35s. Estimated total time: 15h 36m 25s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 38s, 500 more iterations: 7h 48m 12s. +[2025-08-20 17:55:21,169][__main__][INFO] - Starting iteration 628. +[2025-08-20 17:55:44,216][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:55:44,217][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:55:44,224][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:55:46,699][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:55:46,701][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:55:46,707][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:55:46,709][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:55:46,710][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:55:47,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:47,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:48,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:49,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:50,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:50,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:51,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:52,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:53,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:54,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:54,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:55,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:56,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:57,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:58,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:58,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:55:59,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:00,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:01,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:02,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:03,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:04,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:05,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:05,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:06,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:07,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:08,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:08,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:09,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:10,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:11,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:12,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:13,728][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:56:14,679][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:56:14,680][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:56:16,003][__main__][INFO] - Iteration 629 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 28m 8s. Estimated total time: 15h 13m 53s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 23s, 500 more iterations: 7h 36m 56s. +[2025-08-20 17:56:16,004][__main__][INFO] - Starting iteration 629. +[2025-08-20 17:56:39,169][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:56:39,170][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:56:39,177][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:56:41,632][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:56:41,634][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:56:41,640][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:56:41,643][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:56:41,643][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:56:41,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:42,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:43,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:44,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:45,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:45,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:46,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:47,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:48,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:49,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:49,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:50,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:51,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:52,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:53,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:53,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:54,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:55,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:56,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:57,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:58,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:59,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:56:59,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:00,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:01,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:02,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:03,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:03,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:04,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:05,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:06,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:07,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:08,583][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:57:09,547][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:57:09,549][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:57:10,869][__main__][INFO] - Iteration 630 took 54s (37.75% Gen, 62.24% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 27m 44s. Estimated total time: 15h 14m 24s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 12s. +[2025-08-20 17:57:10,871][__main__][INFO] - Starting iteration 630. +[2025-08-20 17:57:34,157][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:57:34,158][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:57:34,165][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:57:36,620][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:57:36,622][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:57:36,628][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:57:36,630][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:57:36,631][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:57:36,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:37,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:38,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:39,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:40,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:40,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:41,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:42,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:43,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:44,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:44,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:45,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:46,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:47,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:48,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:48,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:49,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:50,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:51,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:52,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:52,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:54,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:54,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:55,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:56,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:57,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:58,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:58,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:57:59,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:00,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:01,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:02,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:03,605][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:58:04,664][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:58:04,666][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:58:05,941][__main__][INFO] - Iteration 631 took 55s (37.86% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 30m 15s. Estimated total time: 15h 17m 50s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 55s. +[2025-08-20 17:58:05,943][__main__][INFO] - Starting iteration 631. +[2025-08-20 17:58:29,176][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:58:29,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:58:29,184][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:58:31,644][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:58:31,646][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:58:31,652][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:58:31,654][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:58:31,655][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:58:31,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:32,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:33,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:34,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:35,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:35,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:36,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:37,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:38,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:39,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:39,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:40,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:41,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:42,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:43,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:43,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:44,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:45,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:46,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:47,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:47,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:48,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:49,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:50,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:51,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:51,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:52,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:53,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:54,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:55,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:56,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:57,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:58:58,632][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:58:59,567][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:58:59,568][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:59:00,867][__main__][INFO] - Iteration 632 took 54s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 26m 53s. Estimated total time: 15h 15m 23s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 41s. +[2025-08-20 17:59:00,868][__main__][INFO] - Starting iteration 632. +[2025-08-20 17:59:23,910][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:59:23,912][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:59:23,918][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:59:26,358][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:59:26,359][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:59:26,365][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 17:59:26,368][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 17:59:26,368][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 17:59:26,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:27,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:28,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:29,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:29,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:30,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:31,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:32,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:33,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:33,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:34,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:35,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:36,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:36,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:37,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:38,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:39,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:40,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:40,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:41,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:42,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:43,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:44,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:44,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:45,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:46,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:47,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:48,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:49,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:50,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:50,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:51,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 17:59:53,280][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 17:59:54,248][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 17:59:54,249][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 17:59:55,638][__main__][INFO] - Iteration 633 took 54s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 23m 24s. Estimated total time: 15h 12m 49s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 16s, 500 more iterations: 7h 36m 24s. +[2025-08-20 17:59:55,640][__main__][INFO] - Starting iteration 633. +[2025-08-20 18:00:18,623][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:00:18,624][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:00:18,630][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:00:21,099][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:00:21,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:00:21,107][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:00:21,109][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:00:21,110][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:00:21,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:22,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:22,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:23,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:24,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:25,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:26,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:26,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:27,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:28,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:29,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:30,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:30,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:31,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:32,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:33,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:34,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:34,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:36,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:36,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:37,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:38,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:39,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:40,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:40,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:41,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:42,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:43,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:44,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:44,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:45,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:46,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:00:48,102][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:00:49,050][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:00:49,051][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:00:50,304][__main__][INFO] - Iteration 634 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 20m 44s. Estimated total time: 15h 11m 3s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 6s, 500 more iterations: 7h 35m 31s. +[2025-08-20 18:00:50,306][__main__][INFO] - Starting iteration 634. +[2025-08-20 18:01:13,475][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:01:13,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:01:13,482][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:01:15,929][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:01:15,931][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:01:15,937][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:01:15,939][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:01:15,940][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:01:16,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:17,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:17,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:18,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:19,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:20,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:21,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:21,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:22,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:23,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:24,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:24,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:25,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:26,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:27,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:28,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:28,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:29,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:30,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:31,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:32,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:32,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:34,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:34,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:35,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:36,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:37,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:38,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:38,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:39,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:40,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:41,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:01:42,867][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:01:43,805][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:01:43,806][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:01:45,139][__main__][INFO] - Iteration 635 took 54s (37.82% Gen, 62.18% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 22m 38s. Estimated total time: 15h 13m 52s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 23s, 500 more iterations: 7h 36m 56s. +[2025-08-20 18:01:45,140][__main__][INFO] - Starting iteration 635. +[2025-08-20 18:02:08,472][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:02:08,474][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:02:08,480][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:02:10,956][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:02:10,957][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:02:10,964][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:02:10,966][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:02:10,966][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:02:11,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:12,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:12,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:13,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:14,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:15,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:16,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:16,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:17,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:18,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:19,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:19,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:20,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:21,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:22,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:23,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:23,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:24,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:25,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:26,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:27,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:28,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:29,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:29,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:30,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:31,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:32,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:33,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:33,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:34,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:35,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:36,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:02:37,963][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:02:38,881][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:02:38,883][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:02:40,099][__main__][INFO] - Iteration 636 took 54s (37.97% Gen, 62.03% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 23m 48s. Estimated total time: 15h 15m 58s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 59s. +[2025-08-20 18:02:40,100][__main__][INFO] - Starting iteration 636. +[2025-08-20 18:03:03,181][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:03,182][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:03,188][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:05,634][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:05,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:05,642][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:05,644][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:03:05,644][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:03:05,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:06,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:07,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:08,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:09,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:09,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:10,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:11,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:12,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:13,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:13,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:14,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:15,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:16,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:17,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:17,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:18,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:19,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:20,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:21,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:22,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:23,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:23,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:24,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:25,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:26,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:26,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:27,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:28,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:29,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:30,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:30,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:03:32,555][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:03:33,516][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:03:33,517][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:03:34,860][__main__][INFO] - Iteration 637 took 54s (37.68% Gen, 62.32% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 19m 35s. Estimated total time: 15h 12m 39s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 19s. +[2025-08-20 18:03:34,861][__main__][INFO] - Starting iteration 637. +[2025-08-20 18:03:57,980][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:57,981][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:03:57,988][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:00,456][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:00,457][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:00,463][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:00,466][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:04:00,466][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:04:00,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:01,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:02,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:03,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:03,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:04,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:05,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:06,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:07,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:07,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:08,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:09,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:10,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:11,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:11,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:12,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:13,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:14,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:15,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:15,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:17,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:17,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:18,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:19,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:20,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:21,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:21,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:22,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:23,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:24,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:25,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:25,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:27,427][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:04:28,454][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:04:28,456][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:04:29,845][__main__][INFO] - Iteration 638 took 54s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 22m 24s. Estimated total time: 15h 16m 23s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 11s. +[2025-08-20 18:04:29,847][__main__][INFO] - Starting iteration 638. +[2025-08-20 18:04:52,968][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:52,969][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:52,975][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:55,411][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:55,413][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:55,419][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:04:55,422][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:04:55,422][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:04:55,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:56,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:57,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:58,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:58,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:04:59,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:00,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:01,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:02,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:02,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:03,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:04,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:05,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:06,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:06,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:07,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:08,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:09,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:10,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:10,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:12,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:12,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:13,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:14,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:15,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:16,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:16,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:17,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:18,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:19,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:20,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:20,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:22,448][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:05:23,774][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:05:23,776][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:05:25,658][__main__][INFO] - Iteration 639 took 55s (37.04% Gen, 62.96% Train). Generation: 20s, Training: 35s. Estimated remaining time: 5h 35m 16s. Estimated total time: 15h 30m 10s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 1s, 500 more iterations: 7h 45m 5s. +[2025-08-20 18:05:25,660][__main__][INFO] - Starting iteration 639. +[2025-08-20 18:05:48,755][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:05:48,756][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:05:48,762][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:05:51,226][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:05:51,228][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:05:51,234][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:05:51,236][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:05:51,237][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:05:51,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:52,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:53,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:53,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:54,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:55,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:56,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:57,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:57,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:58,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:05:59,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:00,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:01,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:01,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:02,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:03,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:04,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:05,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:05,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:06,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:07,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:08,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:09,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:10,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:11,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:11,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:12,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:13,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:14,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:14,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:15,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:16,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:18,164][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:06:19,120][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:06:19,122][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:06:20,623][__main__][INFO] - Iteration 640 took 54s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 20m 13s. Estimated total time: 15h 16m 2s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 1s. +[2025-08-20 18:06:20,624][__main__][INFO] - Starting iteration 640. +[2025-08-20 18:06:43,861][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:06:43,863][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:06:43,869][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:06:46,367][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:06:46,368][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:06:46,375][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:06:46,377][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:06:46,378][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:06:46,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:47,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:48,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:49,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:49,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:50,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:51,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:52,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:53,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:53,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:54,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:55,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:56,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:56,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:57,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:58,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:06:59,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:00,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:00,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:02,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:03,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:03,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:04,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:05,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:06,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:06,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:07,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:08,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:09,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:10,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:10,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:11,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:13,340][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:07:14,287][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:07:14,289][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:07:15,593][__main__][INFO] - Iteration 641 took 54s (37.75% Gen, 62.25% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 19m 23s. Estimated total time: 15h 16m 8s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 4s. +[2025-08-20 18:07:15,595][__main__][INFO] - Starting iteration 641. +[2025-08-20 18:07:39,106][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:07:39,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:07:39,114][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:07:41,586][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:07:41,587][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:07:41,594][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:07:41,596][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:07:41,597][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:07:41,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:42,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:43,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:44,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:45,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:45,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:46,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:47,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:48,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:49,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:49,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:50,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:51,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:52,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:53,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:53,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:54,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:55,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:56,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:57,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:58,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:59,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:07:59,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:00,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:01,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:02,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:02,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:03,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:04,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:05,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:06,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:06,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:08,601][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:08:10,191][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:08:10,193][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:08:11,534][__main__][INFO] - Iteration 642 took 55s (37.60% Gen, 62.39% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 34m 38s. Estimated total time: 15h 32m 18s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 13s, 500 more iterations: 7h 46m 9s. +[2025-08-20 18:08:11,535][__main__][INFO] - Starting iteration 642. +[2025-08-20 18:08:34,771][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:08:34,772][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:08:34,779][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:08:37,233][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:08:37,234][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:08:37,241][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:08:37,243][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:08:37,243][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:08:37,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:38,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:39,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:39,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:40,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:41,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:42,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:43,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:43,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:44,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:45,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:46,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:47,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:47,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:48,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:49,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:50,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:51,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:51,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:53,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:53,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:54,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:55,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:56,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:57,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:57,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:58,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:08:59,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:00,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:01,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:01,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:02,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:04,261][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:09:05,217][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:09:05,218][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:09:06,635][__main__][INFO] - Iteration 643 took 55s (37.70% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 19m 43s. Estimated total time: 15h 18m 19s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 9s. +[2025-08-20 18:09:06,637][__main__][INFO] - Starting iteration 643. +[2025-08-20 18:09:30,210][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:09:30,211][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:09:30,218][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:09:32,675][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:09:32,677][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:09:32,683][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:09:32,685][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:09:32,686][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:09:32,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:33,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:34,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:35,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:36,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:36,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:37,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:38,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:39,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:40,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:40,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:41,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:42,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:43,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:44,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:44,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:45,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:46,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:47,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:48,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:48,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:50,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:50,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:51,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:52,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:53,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:54,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:54,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:55,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:56,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:57,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:58,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:09:59,676][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:10:00,608][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:10:00,610][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:10:01,960][__main__][INFO] - Iteration 644 took 55s (38.18% Gen, 61.82% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 22m 32s. Estimated total time: 15h 22m 2s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 12s, 500 more iterations: 7h 41m 1s. +[2025-08-20 18:10:01,961][__main__][INFO] - Starting iteration 644. +[2025-08-20 18:10:25,121][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:10:25,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:10:25,128][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:10:27,605][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:10:27,607][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:10:27,613][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:10:27,615][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:10:27,616][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:10:27,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:28,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:29,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:30,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:31,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:31,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:32,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:33,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:34,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:35,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:35,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:36,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:37,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:38,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:39,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:39,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:40,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:41,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:42,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:43,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:44,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:45,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:45,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:46,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:47,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:48,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:48,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:49,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:50,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:51,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:52,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:52,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:10:54,543][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:10:55,563][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:10:55,566][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:10:57,036][__main__][INFO] - Iteration 645 took 55s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 17m 28s. Estimated total time: 15h 17m 54s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 47s, 500 more iterations: 7h 38m 57s. +[2025-08-20 18:10:57,038][__main__][INFO] - Starting iteration 645. +[2025-08-20 18:11:20,786][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:11:20,787][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:11:20,793][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:11:23,254][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:11:23,255][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:11:23,262][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:11:23,264][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:11:23,265][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:11:23,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:24,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:25,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:25,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:26,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:27,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:28,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:29,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:29,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:30,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:31,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:32,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:33,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:33,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:34,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:35,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:36,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:37,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:37,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:38,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:39,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:40,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:41,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:42,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:43,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:43,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:44,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:45,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:46,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:47,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:47,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:48,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:11:50,242][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:11:51,209][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:11:51,210][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:11:52,530][__main__][INFO] - Iteration 646 took 55s (38.36% Gen, 61.63% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 23m 29s. Estimated total time: 15h 24m 51s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 29s, 500 more iterations: 7h 42m 25s. +[2025-08-20 18:11:52,531][__main__][INFO] - Starting iteration 646. +[2025-08-20 18:12:15,623][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:12:15,625][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:12:15,631][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:12:18,086][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:12:18,087][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:12:18,093][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:12:18,096][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:12:18,096][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:12:18,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:19,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:19,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:20,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:21,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:22,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:23,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:23,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:24,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:25,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:26,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:27,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:27,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:28,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:29,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:30,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:31,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:31,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:32,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:33,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:34,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:35,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:35,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:36,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:37,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:38,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:39,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:40,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:41,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:41,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:42,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:43,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:12:45,121][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:12:46,057][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:12:46,059][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:12:47,375][__main__][INFO] - Iteration 647 took 54s (37.65% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 11m 47s. Estimated total time: 15h 14m 3s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 1s. +[2025-08-20 18:12:47,377][__main__][INFO] - Starting iteration 647. +[2025-08-20 18:13:13,155][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:13:13,157][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:13:13,163][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:13:15,634][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:13:15,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:13:15,642][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:13:15,645][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:13:15,646][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:13:15,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:16,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:17,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:18,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:19,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:19,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:20,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:21,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:22,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:23,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:24,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:25,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:26,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:26,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:27,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:28,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:29,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:30,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:30,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:31,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:32,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:33,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:33,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:35,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:35,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:36,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:37,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:38,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:39,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:39,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:40,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:41,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:13:43,123][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:13:44,089][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:13:44,091][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:13:45,548][__main__][INFO] - Iteration 648 took 58s (40.09% Gen, 59.91% Train). Generation: 23s, Training: 34s. Estimated remaining time: 6h 6m 16s. Estimated total time: 16h 9m 30s. Time estimates for 10 more iterations: 9m 41s, 100 more iterations: 1h 36m 57s, 500 more iterations: 8h 4m 45s. +[2025-08-20 18:13:45,550][__main__][INFO] - Starting iteration 648. +[2025-08-20 18:14:08,799][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:14:08,800][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:14:08,806][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:14:11,283][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:14:11,284][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:14:11,291][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:14:11,293][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:14:11,294][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:14:11,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:12,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:13,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:13,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:14,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:15,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:16,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:17,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:17,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:18,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:19,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:20,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:21,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:21,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:22,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:23,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:24,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:25,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:25,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:26,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:27,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:28,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:29,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:30,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:31,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:31,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:32,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:33,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:34,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:35,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:35,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:36,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:14:38,297][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:14:39,263][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:14:39,264][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:14:40,486][__main__][INFO] - Iteration 649 took 54s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 11m 26s. Estimated total time: 15h 15m 36s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 48s. +[2025-08-20 18:14:40,487][__main__][INFO] - Starting iteration 649. +[2025-08-20 18:15:03,595][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:03,596][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:03,602][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:06,034][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:06,035][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:06,042][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:06,044][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:15:06,045][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:15:06,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:07,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:07,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:08,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:09,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:10,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:11,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:11,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:12,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:13,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:14,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:15,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:15,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:16,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:17,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:18,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:19,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:19,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:21,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:21,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:22,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:23,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:24,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:25,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:25,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:26,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:27,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:28,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:28,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:29,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:30,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:31,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:15:32,917][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:15:33,924][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:15:33,926][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:15:35,272][__main__][INFO] - Iteration 650 took 54s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 8m 0s. Estimated total time: 15h 13m 4s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 18s, 500 more iterations: 7h 36m 32s. +[2025-08-20 18:15:35,274][__main__][INFO] - Starting iteration 650. +[2025-08-20 18:15:58,756][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:58,757][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:15:58,763][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:01,243][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:01,244][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:01,251][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:01,253][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:16:01,253][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:16:01,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:02,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:03,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:03,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:04,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:05,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:06,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:07,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:07,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:08,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:09,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:10,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:11,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:11,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:12,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:13,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:14,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:15,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:16,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:17,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:17,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:18,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:19,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:20,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:21,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:21,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:22,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:23,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:24,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:25,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:25,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:26,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:16:28,277][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:16:29,277][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:16:29,279][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:16:33,231][__main__][INFO] - Iteration 651 took 57s (36.27% Gen, 59.15% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 59m 54s. Estimated total time: 16h 5m 56s. Time estimates for 10 more iterations: 9m 39s, 100 more iterations: 1h 36m 35s, 500 more iterations: 8h 2m 58s. +[2025-08-20 18:16:33,232][__main__][INFO] - Starting iteration 651. +[2025-08-20 18:16:56,474][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:56,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:56,482][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:58,960][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:58,961][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:58,968][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:16:58,970][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:16:58,971][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:16:59,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:00,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:00,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:01,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:02,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:03,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:04,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:04,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:05,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:06,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:07,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:07,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:08,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:09,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:10,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:11,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:11,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:12,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:13,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:14,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:15,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:15,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:17,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:17,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:18,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:19,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:20,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:21,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:21,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:22,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:23,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:24,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:25,904][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:17:26,833][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:17:26,834][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:17:28,203][__main__][INFO] - Iteration 652 took 54s (37.79% Gen, 62.21% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 9m 13s. Estimated total time: 15h 16m 10s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 5s. +[2025-08-20 18:17:28,205][__main__][INFO] - Starting iteration 652. +[2025-08-20 18:17:51,455][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:17:51,456][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:17:51,463][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:17:53,921][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:17:53,922][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:17:53,929][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:17:53,931][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:17:53,932][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:17:54,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:55,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:55,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:56,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:57,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:58,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:58,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:17:59,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:00,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:01,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:02,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:02,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:03,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:04,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:05,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:06,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:06,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:07,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:08,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:09,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:10,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:11,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:12,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:13,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:13,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:14,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:15,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:16,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:17,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:17,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:18,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:19,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:20,960][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:18:21,927][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:18:21,929][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:18:23,333][__main__][INFO] - Iteration 653 took 55s (37.71% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 10m 55s. Estimated total time: 15h 18m 47s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 23s. +[2025-08-20 18:18:23,334][__main__][INFO] - Starting iteration 653. +[2025-08-20 18:18:46,590][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:18:46,591][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:18:46,597][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:18:49,058][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:18:49,059][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:18:49,065][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:18:49,067][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:18:49,068][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:18:49,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:50,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:50,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:51,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:52,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:53,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:54,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:54,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:55,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:56,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:57,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:58,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:58,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:18:59,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:00,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:01,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:02,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:02,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:03,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:04,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:05,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:06,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:06,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:07,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:08,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:09,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:10,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:11,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:12,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:12,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:13,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:14,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:16,110][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:19:17,075][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:19:17,077][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:19:18,321][__main__][INFO] - Iteration 654 took 54s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 7m 38s. Estimated total time: 15h 16m 26s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 13s. +[2025-08-20 18:19:18,322][__main__][INFO] - Starting iteration 654. +[2025-08-20 18:19:41,522][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:19:41,523][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:19:41,530][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:19:43,984][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:19:43,985][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:19:43,992][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:19:43,994][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:19:43,994][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:19:44,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:45,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:45,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:46,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:47,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:48,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:49,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:49,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:50,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:51,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:52,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:53,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:53,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:54,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:55,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:56,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:57,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:58,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:59,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:19:59,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:00,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:01,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:02,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:02,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:03,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:04,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:05,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:06,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:06,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:07,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:08,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:09,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:10,931][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:20:11,884][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:20:11,886][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:20:13,254][__main__][INFO] - Iteration 655 took 54s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 5m 48s. Estimated total time: 15h 15m 31s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 45s. +[2025-08-20 18:20:13,260][__main__][INFO] - Starting iteration 655. +[2025-08-20 18:20:36,873][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:20:36,878][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:20:36,888][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:20:39,356][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:20:39,357][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:20:39,364][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:20:39,366][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:20:39,367][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:20:39,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:40,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:41,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:42,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:42,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:43,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:44,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:45,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:46,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:46,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:47,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:48,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:49,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:50,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:50,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:51,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:52,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:53,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:53,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:55,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:56,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:56,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:57,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:58,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:20:59,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:00,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:00,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:01,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:02,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:03,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:04,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:04,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:06,389][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:21:07,321][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:21:07,322][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:21:08,589][__main__][INFO] - Iteration 656 took 55s (38.20% Gen, 61.80% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 11m 24s. Estimated total time: 15h 22m 2s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 12s, 500 more iterations: 7h 41m 1s. +[2025-08-20 18:21:08,591][__main__][INFO] - Starting iteration 656. +[2025-08-20 18:21:31,825][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:21:31,826][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:21:31,832][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:21:34,257][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:21:34,258][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:21:34,264][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:21:34,267][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:21:34,267][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:21:34,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:35,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:36,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:36,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:37,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:38,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:39,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:40,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:40,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:41,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:42,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:43,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:44,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:44,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:45,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:46,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:47,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:48,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:48,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:49,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:50,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:51,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:52,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:53,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:54,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:54,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:55,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:56,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:57,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:58,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:58,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:21:59,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:01,183][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:22:02,079][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:22:02,080][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:22:03,368][__main__][INFO] - Iteration 657 took 54s (37.96% Gen, 62.04% Train). Generation: 20s, Training: 33s. Estimated remaining time: 5h 1m 25s. Estimated total time: 15h 12m 57s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 17s, 500 more iterations: 7h 36m 28s. +[2025-08-20 18:22:03,370][__main__][INFO] - Starting iteration 657. +[2025-08-20 18:22:26,612][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:22:26,613][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:22:26,620][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:22:29,073][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:22:29,074][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:22:29,081][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:22:29,083][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:22:29,084][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:22:29,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:30,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:30,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:31,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:32,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:33,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:34,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:34,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:35,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:36,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:37,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:38,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:38,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:39,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:40,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:41,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:42,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:42,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:43,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:44,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:45,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:46,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:47,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:48,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:48,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:49,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:50,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:51,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:52,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:52,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:53,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:54,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:22:56,099][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:22:57,069][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:22:57,071][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:22:58,404][__main__][INFO] - Iteration 658 took 55s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 4m 46s. Estimated total time: 15h 17m 13s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 36s. +[2025-08-20 18:22:58,405][__main__][INFO] - Starting iteration 658. +[2025-08-20 18:23:21,634][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:23:21,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:23:21,641][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:23:24,093][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:23:24,094][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:23:24,101][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:23:24,103][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:23:24,104][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:23:24,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:25,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:25,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:26,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:27,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:28,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:29,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:29,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:30,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:31,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:32,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:33,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:33,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:34,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:35,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:36,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:37,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:37,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:38,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:39,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:40,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:41,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:41,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:42,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:43,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:44,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:45,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:46,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:47,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:47,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:48,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:49,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:23:51,110][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:23:52,064][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:23:52,066][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:23:53,333][__main__][INFO] - Iteration 659 took 54s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 2m 4s. Estimated total time: 15h 15m 27s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 43s. +[2025-08-20 18:23:53,334][__main__][INFO] - Starting iteration 659. +[2025-08-20 18:24:16,771][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:24:16,773][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:24:16,779][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:24:19,291][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:24:19,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:24:19,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:24:19,301][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:24:19,302][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:24:19,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:20,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:21,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:21,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:22,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:23,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:24,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:25,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:25,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:26,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:27,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:28,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:29,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:29,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:30,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:31,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:32,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:33,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:33,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:34,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:35,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:36,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:37,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:38,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:39,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:39,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:40,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:41,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:42,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:43,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:43,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:44,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:24:46,179][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:24:47,136][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:24:47,137][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:24:48,441][__main__][INFO] - Iteration 660 took 55s (38.01% Gen, 61.98% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 4m 9s. Estimated total time: 15h 18m 26s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 50s, 500 more iterations: 7h 39m 13s. +[2025-08-20 18:24:48,443][__main__][INFO] - Starting iteration 660. +[2025-08-20 18:25:12,294][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:25:12,296][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:25:12,302][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:25:14,767][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:25:14,769][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:25:14,776][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:25:14,778][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:25:14,778][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:25:15,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:15,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:16,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:17,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:18,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:19,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:19,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:20,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:21,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:22,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:23,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:23,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:24,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:25,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:26,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:26,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:27,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:28,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:29,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:30,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:30,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:31,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:32,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:33,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:34,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:35,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:36,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:37,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:37,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:38,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:39,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:40,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:25:41,915][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:25:42,856][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:25:42,858][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:25:44,116][__main__][INFO] - Iteration 661 took 55s (38.40% Gen, 61.60% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 12m 39s. Estimated total time: 15h 27m 52s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 47s, 500 more iterations: 7h 43m 56s. +[2025-08-20 18:25:44,117][__main__][INFO] - Starting iteration 661. +[2025-08-20 18:26:07,385][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:26:07,386][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:26:07,393][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:26:09,815][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:26:09,816][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:26:09,823][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:26:09,825][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:26:09,825][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:26:10,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:10,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:11,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:12,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:13,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:14,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:14,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:15,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:16,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:17,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:18,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:18,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:19,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:20,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:21,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:22,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:22,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:23,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:24,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:25,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:26,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:27,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:27,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:28,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:29,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:30,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:31,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:31,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:32,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:33,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:34,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:35,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:26:36,727][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:26:37,734][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:26:37,736][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:26:39,079][__main__][INFO] - Iteration 662 took 54s (37.88% Gen, 62.12% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 59m 52s. Estimated total time: 15h 16m 0s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 0s. +[2025-08-20 18:26:39,080][__main__][INFO] - Starting iteration 662. +[2025-08-20 18:27:02,236][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:02,237][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:02,243][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:04,691][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:04,692][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:04,699][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:04,701][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:27:04,701][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:27:05,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:05,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:06,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:07,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:08,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:08,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:09,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:10,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:11,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:12,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:12,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:13,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:14,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:15,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:16,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:16,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:17,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:18,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:19,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:20,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:21,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:22,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:23,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:23,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:24,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:25,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:26,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:26,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:27,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:28,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:29,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:30,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:27:31,752][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:27:32,700][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:27:32,702][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:27:34,170][__main__][INFO] - Iteration 663 took 55s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 1m 6s. Estimated total time: 15h 18m 9s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 4s. +[2025-08-20 18:27:34,171][__main__][INFO] - Starting iteration 663. +[2025-08-20 18:27:57,422][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:57,424][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:57,430][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:59,898][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:59,899][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:59,906][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:27:59,909][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:27:59,909][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:28:00,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:01,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:01,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:02,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:03,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:04,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:04,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:05,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:06,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:07,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:08,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:08,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:09,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:10,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:11,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:12,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:12,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:13,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:14,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:15,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:16,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:16,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:17,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:18,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:19,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:20,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:21,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:22,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:22,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:23,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:24,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:25,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:26,962][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:28:28,406][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:28:28,408][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:28:29,653][__main__][INFO] - Iteration 664 took 55s (37.47% Gen, 62.53% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 6m 38s. Estimated total time: 15h 24m 37s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 27s, 500 more iterations: 7h 42m 18s. +[2025-08-20 18:28:29,655][__main__][INFO] - Starting iteration 664. +[2025-08-20 18:28:53,024][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:28:53,025][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:28:53,031][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:28:55,465][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:28:55,466][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:28:55,473][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:28:55,475][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:28:55,476][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:28:55,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:56,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:57,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:58,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:58,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:28:59,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:00,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:01,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:02,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:02,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:03,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:04,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:05,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:06,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:06,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:07,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:09,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:09,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:10,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:11,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:12,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:13,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:14,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:15,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:15,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:16,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:17,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:18,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:19,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:19,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:20,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:21,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:22,958][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:29:23,910][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:29:23,912][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:29:25,295][__main__][INFO] - Iteration 665 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 8m 25s. Estimated total time: 15h 27m 19s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 43s, 500 more iterations: 7h 43m 39s. +[2025-08-20 18:29:25,297][__main__][INFO] - Starting iteration 665. +[2025-08-20 18:29:48,877][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:29:48,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:29:48,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:29:51,346][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:29:51,347][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:29:51,354][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:29:51,356][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:29:51,356][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:29:51,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:52,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:53,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:54,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:54,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:55,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:56,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:57,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:57,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:58,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:29:59,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:00,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:01,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:01,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:02,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:03,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:04,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:05,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:05,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:06,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:07,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:08,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:09,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:10,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:11,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:12,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:12,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:13,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:14,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:15,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:16,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:16,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:18,427][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:30:19,376][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:30:19,378][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:30:20,688][__main__][INFO] - Iteration 666 took 55s (38.15% Gen, 61.85% Train). Generation: 21s, Training: 34s. Estimated remaining time: 5h 3m 21s. Estimated total time: 15h 23m 11s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 35s. +[2025-08-20 18:30:20,693][__main__][INFO] - Starting iteration 666. +[2025-08-20 18:30:43,977][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:30:43,979][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:30:43,985][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:30:46,421][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:30:46,423][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:30:46,430][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:30:46,431][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:30:46,432][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:30:46,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:47,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:48,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:49,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:49,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:50,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:51,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:52,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:53,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:53,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:54,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:55,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:56,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:57,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:57,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:58,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:30:59,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:00,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:00,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:01,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:03,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:03,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:04,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:05,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:06,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:06,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:07,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:08,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:09,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:10,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:10,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:11,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:13,322][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:31:14,246][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:31:14,247][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:31:15,574][__main__][INFO] - Iteration 667 took 54s (37.96% Gen, 62.04% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 53m 56s. Estimated total time: 15h 14m 40s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 20s. +[2025-08-20 18:31:15,575][__main__][INFO] - Starting iteration 667. +[2025-08-20 18:31:39,155][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:31:39,156][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:31:39,162][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:31:41,631][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:31:41,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:31:41,639][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:31:41,641][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:31:41,641][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:31:41,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:42,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:43,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:44,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:45,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:45,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:46,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:47,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:48,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:49,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:49,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:50,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:52,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:53,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:54,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:54,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:55,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:56,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:57,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:57,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:58,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:31:59,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:00,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:01,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:02,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:03,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:04,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:04,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:05,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:06,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:07,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:07,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:09,553][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:32:10,691][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:32:10,693][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:32:12,096][__main__][INFO] - Iteration 668 took 56s (37.35% Gen, 62.65% Train). Generation: 21s, Training: 35s. Estimated remaining time: 5h 20m 19s. Estimated total time: 15h 42m 0s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 12s, 500 more iterations: 7h 51m 0s. +[2025-08-20 18:32:12,098][__main__][INFO] - Starting iteration 668. +[2025-08-20 18:32:35,323][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:32:35,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:32:35,331][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:32:37,787][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:32:37,788][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:32:37,795][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:32:37,797][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:32:37,797][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:32:38,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:38,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:39,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:40,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:41,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:42,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:42,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:43,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:44,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:45,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:46,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:46,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:47,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:48,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:49,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:50,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:50,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:51,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:52,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:53,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:53,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:54,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:55,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:56,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:57,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:57,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:32:59,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:00,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:00,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:01,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:02,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:03,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:04,800][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:33:05,791][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:33:05,793][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:33:07,489][__main__][INFO] - Iteration 669 took 55s (37.55% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 0m 33s. Estimated total time: 15h 23m 10s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 35s. +[2025-08-20 18:33:07,490][__main__][INFO] - Starting iteration 669. +[2025-08-20 18:33:30,740][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:33:30,741][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:33:30,747][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:33:33,184][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:33:33,186][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:33:33,192][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:33:33,194][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:33:33,195][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:33:33,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:34,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:35,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:35,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:36,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:37,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:38,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:39,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:39,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:40,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:41,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:42,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:43,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:43,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:44,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:45,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:46,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:46,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:47,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:48,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:49,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:50,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:51,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:52,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:52,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:53,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:54,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:55,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:56,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:56,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:57,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:33:58,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:00,100][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:34:01,069][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:34:01,071][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:34:02,409][__main__][INFO] - Iteration 670 took 54s (37.89% Gen, 62.11% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 51m 47s. Estimated total time: 15h 15m 18s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 39s. +[2025-08-20 18:34:02,411][__main__][INFO] - Starting iteration 670. +[2025-08-20 18:34:26,495][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:34:26,496][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:34:26,502][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:34:28,958][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:34:28,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:34:28,965][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:34:28,968][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:34:28,968][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:34:29,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:30,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:30,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:31,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:32,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:33,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:34,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:34,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:35,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:36,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:37,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:37,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:38,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:39,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:40,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:41,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:41,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:42,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:44,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:44,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:45,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:46,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:47,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:48,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:48,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:49,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:50,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:51,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:52,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:52,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:53,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:54,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:34:56,011][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:34:56,986][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:34:56,988][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:34:59,058][__main__][INFO] - Iteration 671 took 56s (38.17% Gen, 61.83% Train). Generation: 21s, Training: 35s. Estimated remaining time: 5h 19m 38s. Estimated total time: 15h 44m 6s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 24s, 500 more iterations: 7h 52m 3s. +[2025-08-20 18:34:59,059][__main__][INFO] - Starting iteration 671. +[2025-08-20 18:35:22,697][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:35:22,698][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:35:22,705][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:35:25,136][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:35:25,137][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:35:25,143][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:35:25,145][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:35:25,146][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:35:25,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:26,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:27,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:27,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:28,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:29,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:30,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:30,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:31,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:32,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:33,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:34,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:34,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:35,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:36,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:37,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:38,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:38,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:39,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:40,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:41,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:42,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:42,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:44,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:44,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:45,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:46,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:47,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:48,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:48,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:49,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:50,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:35:52,144][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:35:53,118][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:35:53,119][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:35:54,467][__main__][INFO] - Iteration 672 took 55s (38.26% Gen, 61.74% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 58m 3s. Estimated total time: 15h 23m 27s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 20s, 500 more iterations: 7h 41m 43s. +[2025-08-20 18:35:54,469][__main__][INFO] - Starting iteration 672. +[2025-08-20 18:36:18,059][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:36:18,061][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:36:18,067][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:36:20,521][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:36:20,522][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:36:20,528][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:36:20,531][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:36:20,531][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:36:20,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:21,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:22,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:23,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:23,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:24,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:25,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:26,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:27,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:27,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:28,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:29,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:30,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:31,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:31,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:32,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:34,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:34,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:35,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:36,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:37,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:37,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:38,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:39,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:40,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:41,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:41,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:42,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:43,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:44,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:45,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:45,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:36:47,524][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:36:48,478][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:36:48,480][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:36:49,838][__main__][INFO] - Iteration 673 took 55s (38.19% Gen, 61.81% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 56m 30s. Estimated total time: 15h 22m 49s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 24s. +[2025-08-20 18:36:49,840][__main__][INFO] - Starting iteration 673. +[2025-08-20 18:37:13,156][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:37:13,157][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:37:13,164][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:37:15,613][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:37:15,615][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:37:15,621][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:37:15,623][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:37:15,624][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:37:15,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:16,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:17,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:18,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:19,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:19,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:20,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:21,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:22,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:23,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:23,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:24,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:25,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:26,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:27,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:27,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:28,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:29,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:30,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:31,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:31,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:32,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:33,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:34,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:35,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:36,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:37,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:37,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:38,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:39,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:40,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:41,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:37:42,673][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:37:43,629][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:37:43,631][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:37:45,133][__main__][INFO] - Iteration 674 took 55s (37.67% Gen, 62.33% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 54m 18s. Estimated total time: 15h 21m 32s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 9s, 500 more iterations: 7h 40m 46s. +[2025-08-20 18:37:45,135][__main__][INFO] - Starting iteration 674. +[2025-08-20 18:38:08,517][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:38:08,518][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:38:08,524][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:38:10,990][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:38:10,991][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:38:10,997][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:38:10,999][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:38:11,000][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:38:11,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:12,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:12,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:13,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:14,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:15,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:16,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:16,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:17,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:18,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:19,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:20,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:20,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:21,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:22,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:23,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:24,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:24,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:25,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:26,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:27,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:28,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:29,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:29,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:30,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:31,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:32,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:33,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:33,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:34,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:35,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:36,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:38:37,969][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:38:38,899][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:38:38,901][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:38:40,612][__main__][INFO] - Iteration 675 took 55s (37.71% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 56m 27s. Estimated total time: 15h 24m 37s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 27s, 500 more iterations: 7h 42m 18s. +[2025-08-20 18:38:40,614][__main__][INFO] - Starting iteration 675. +[2025-08-20 18:39:04,206][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:04,207][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:04,214][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:06,681][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:06,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:06,689][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:06,691][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:39:06,691][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:39:06,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:07,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:08,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:09,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:10,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:10,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:11,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:12,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:13,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:14,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:14,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:15,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:16,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:17,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:18,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:18,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:19,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:20,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:21,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:22,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:23,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:24,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:24,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:25,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:26,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:27,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:28,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:28,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:29,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:30,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:31,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:32,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:39:33,750][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:39:34,680][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:39:34,682][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:39:36,101][__main__][INFO] - Iteration 676 took 55s (38.12% Gen, 61.88% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 55m 41s. Estimated total time: 15h 24m 46s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 28s, 500 more iterations: 7h 42m 23s. +[2025-08-20 18:39:36,102][__main__][INFO] - Starting iteration 676. +[2025-08-20 18:39:59,651][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:59,652][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:39:59,658][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:02,109][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:02,110][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:02,116][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:02,119][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:40:02,119][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:40:02,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:03,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:04,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:04,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:05,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:06,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:07,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:07,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:08,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:09,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:10,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:11,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:11,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:12,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:13,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:14,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:15,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:15,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:16,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:17,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:18,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:19,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:20,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:21,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:21,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:22,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:23,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:24,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:25,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:25,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:26,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:27,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:29,096][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:40:30,021][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:40:30,022][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:40:31,361][__main__][INFO] - Iteration 677 took 55s (38.17% Gen, 61.83% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 50m 57s. Estimated total time: 15h 20m 58s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 5s, 500 more iterations: 7h 40m 29s. +[2025-08-20 18:40:31,362][__main__][INFO] - Starting iteration 677. +[2025-08-20 18:40:55,032][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:55,033][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:55,040][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:57,495][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:57,496][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:57,503][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:40:57,505][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:40:57,506][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:40:57,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:58,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:40:59,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:00,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:00,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:01,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:02,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:03,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:04,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:04,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:05,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:06,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:07,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:08,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:08,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:09,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:10,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:11,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:12,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:12,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:14,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:14,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:15,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:16,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:17,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:18,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:18,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:19,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:20,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:21,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:22,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:22,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:24,535][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:41:25,488][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:41:25,489][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:41:26,753][__main__][INFO] - Iteration 678 took 55s (38.34% Gen, 61.66% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 52m 14s. Estimated total time: 15h 23m 10s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 35s. +[2025-08-20 18:41:26,754][__main__][INFO] - Starting iteration 678. +[2025-08-20 18:41:49,934][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:41:49,935][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:41:49,942][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:41:52,403][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:41:52,404][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:41:52,411][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:41:52,413][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:41:52,413][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:41:52,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:53,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:54,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:55,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:55,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:56,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:57,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:58,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:59,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:41:59,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:00,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:01,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:02,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:03,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:03,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:04,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:05,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:06,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:06,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:08,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:09,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:09,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:10,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:11,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:12,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:13,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:13,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:14,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:15,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:16,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:17,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:17,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:19,561][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:42:20,529][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:42:20,531][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:42:21,815][__main__][INFO] - Iteration 679 took 55s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 45m 49s. Estimated total time: 15h 17m 40s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 50s. +[2025-08-20 18:42:21,816][__main__][INFO] - Starting iteration 679. +[2025-08-20 18:42:45,038][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:42:45,040][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:42:45,046][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:42:47,498][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:42:47,499][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:42:47,506][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:42:47,508][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:42:47,508][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:42:47,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:48,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:49,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:50,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:50,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:51,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:52,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:53,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:54,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:54,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:55,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:56,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:57,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:58,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:58,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:42:59,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:00,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:01,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:02,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:03,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:04,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:04,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:05,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:06,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:07,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:08,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:08,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:09,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:10,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:11,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:12,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:12,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:14,461][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:43:15,383][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:43:15,384][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:43:16,734][__main__][INFO] - Iteration 680 took 54s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 42m 31s. Estimated total time: 15h 15m 17s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 38s. +[2025-08-20 18:43:16,735][__main__][INFO] - Starting iteration 680. +[2025-08-20 18:43:40,224][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:43:40,226][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:43:40,232][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:43:42,684][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:43:42,685][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:43:42,692][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:43:42,694][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:43:42,695][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:43:42,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:43,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:44,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:45,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:46,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:46,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:47,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:48,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:49,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:50,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:50,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:51,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:52,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:53,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:54,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:54,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:55,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:56,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:57,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:58,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:43:59,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:00,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:00,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:01,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:03,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:04,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:05,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:06,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:07,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:07,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:08,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:09,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:11,060][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:44:11,973][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:44:11,975][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:44:13,254][__main__][INFO] - Iteration 681 took 56s (37.26% Gen, 62.74% Train). Generation: 21s, Training: 35s. Estimated remaining time: 5h 8m 15s. Estimated total time: 15h 41m 58s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 11s, 500 more iterations: 7h 50m 59s. +[2025-08-20 18:44:13,255][__main__][INFO] - Starting iteration 681. +[2025-08-20 18:44:36,846][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:44:36,847][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:44:36,853][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:44:39,290][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:44:39,291][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:44:39,297][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:44:39,299][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:44:39,300][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:44:39,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:40,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:41,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:41,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:42,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:43,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:44,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:45,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:45,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:46,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:47,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:48,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:49,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:49,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:50,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:51,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:52,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:53,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:54,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:55,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:55,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:56,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:57,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:58,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:59,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:44:59,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:00,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:01,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:02,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:03,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:03,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:04,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:06,305][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:45:07,313][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:45:07,315][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:45:08,760][__main__][INFO] - Iteration 682 took 55s (38.12% Gen, 61.88% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 50m 26s. Estimated total time: 15h 25m 4s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 30s, 500 more iterations: 7h 42m 32s. +[2025-08-20 18:45:08,761][__main__][INFO] - Starting iteration 682. +[2025-08-20 18:45:32,000][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:45:32,002][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:45:32,008][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:45:34,467][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:45:34,469][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:45:34,475][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:45:34,477][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:45:34,478][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:45:34,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:35,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:36,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:37,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:37,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:38,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:39,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:40,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:41,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:41,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:42,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:43,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:44,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:45,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:45,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:46,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:47,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:48,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:49,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:50,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:51,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:51,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:52,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:53,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:54,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:55,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:55,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:56,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:57,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:58,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:45:59,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:00,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:01,973][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:46:02,927][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:46:02,929][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:46:04,233][__main__][INFO] - Iteration 683 took 55s (37.47% Gen, 62.53% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 48m 58s. Estimated total time: 15h 24m 31s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 27s, 500 more iterations: 7h 42m 15s. +[2025-08-20 18:46:04,234][__main__][INFO] - Starting iteration 683. +[2025-08-20 18:46:27,334][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:46:27,335][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:46:27,341][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:46:29,819][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:46:29,820][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:46:29,827][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:46:29,829][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:46:29,829][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:46:30,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:30,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:31,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:32,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:33,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:34,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:34,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:35,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:36,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:37,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:38,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:38,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:39,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:40,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:41,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:42,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:42,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:43,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:44,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:45,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:45,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:46,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:48,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:48,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:49,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:50,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:51,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:52,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:52,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:53,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:54,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:55,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:46:56,911][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:46:57,832][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:46:57,833][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:46:59,114][__main__][INFO] - Iteration 684 took 54s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 38m 11s. Estimated total time: 15h 14m 39s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 19s. +[2025-08-20 18:46:59,115][__main__][INFO] - Starting iteration 684. +[2025-08-20 18:47:22,312][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:47:22,313][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:47:22,319][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:47:24,770][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:47:24,771][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:47:24,778][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:47:24,779][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:47:24,780][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:47:25,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:25,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:26,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:27,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:28,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:29,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:29,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:30,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:31,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:32,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:33,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:33,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:34,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:35,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:36,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:36,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:37,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:38,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:39,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:40,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:41,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:42,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:43,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:43,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:45,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:45,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:46,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:47,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:48,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:49,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:49,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:50,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:47:52,293][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:47:53,218][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:47:53,219][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:47:54,584][__main__][INFO] - Iteration 685 took 55s (37.41% Gen, 62.59% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 47m 4s. Estimated total time: 15h 24m 27s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 26s, 500 more iterations: 7h 42m 13s. +[2025-08-20 18:47:54,585][__main__][INFO] - Starting iteration 685. +[2025-08-20 18:48:18,063][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:48:18,064][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:48:18,070][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:48:20,500][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:48:20,501][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:48:20,508][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:48:20,510][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:48:20,510][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:48:20,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:21,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:22,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:23,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:23,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:24,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:25,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:26,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:27,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:27,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:28,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:29,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:30,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:31,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:31,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:32,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:33,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:34,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:35,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:35,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:36,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:37,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:38,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:39,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:40,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:41,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:42,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:42,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:43,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:44,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:45,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:45,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:48:47,600][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:48:48,522][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:48:48,523][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:48:49,768][__main__][INFO] - Iteration 686 took 55s (38.14% Gen, 61.86% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 41m 23s. Estimated total time: 15h 19m 42s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 51s. +[2025-08-20 18:48:49,769][__main__][INFO] - Starting iteration 686. +[2025-08-20 18:49:12,869][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:49:12,870][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:49:12,876][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:49:15,327][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:49:15,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:49:15,335][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:49:15,337][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:49:15,338][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:49:15,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:16,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:17,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:18,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:18,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:19,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:20,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:21,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:21,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:23,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:24,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:25,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:26,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:26,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:27,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:28,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:29,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:30,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:31,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:32,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:32,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:33,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:34,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:35,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:36,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:36,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:37,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:38,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:39,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:39,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:40,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:41,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:49:43,248][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:49:44,199][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:49:44,201][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:49:45,517][__main__][INFO] - Iteration 687 took 55s (37.03% Gen, 62.96% Train). Generation: 20s, Training: 35s. Estimated remaining time: 4h 49m 52s. Estimated total time: 15h 29m 7s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 54s, 500 more iterations: 7h 44m 33s. +[2025-08-20 18:49:45,518][__main__][INFO] - Starting iteration 687. +[2025-08-20 18:50:08,681][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:50:08,682][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:50:08,688][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:50:11,137][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:50:11,138][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:50:11,144][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:50:11,147][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:50:11,147][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:50:11,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:12,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:13,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:13,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:14,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:15,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:16,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:16,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:17,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:18,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:19,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:20,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:20,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:21,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:22,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:23,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:24,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:24,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:25,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:26,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:27,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:28,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:28,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:30,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:31,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:31,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:32,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:33,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:34,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:34,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:35,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:36,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:50:38,195][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:50:39,098][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:50:39,099][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:50:40,461][__main__][INFO] - Iteration 688 took 54s (37.70% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 35m 32s. Estimated total time: 15h 15m 42s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 34s, 500 more iterations: 7h 37m 51s. +[2025-08-20 18:50:40,462][__main__][INFO] - Starting iteration 688. +[2025-08-20 18:51:03,520][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:03,522][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:03,528][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:05,981][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:05,982][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:05,988][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:05,990][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:51:05,991][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:51:06,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:07,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:07,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:08,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:09,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:10,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:11,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:11,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:12,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:13,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:14,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:15,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:15,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:16,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:17,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:18,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:19,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:19,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:20,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:21,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:22,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:23,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:24,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:25,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:25,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:26,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:27,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:28,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:29,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:29,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:30,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:31,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:51:33,215][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:51:34,195][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:51:34,196][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:51:35,434][__main__][INFO] - Iteration 689 took 54s (37.50% Gen, 62.50% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 35m 6s. Estimated total time: 15h 16m 10s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 5s. +[2025-08-20 18:51:35,435][__main__][INFO] - Starting iteration 689. +[2025-08-20 18:51:58,567][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:58,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:51:58,574][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:01,017][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:01,018][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:01,025][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:01,027][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:52:01,027][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:52:01,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:02,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:02,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:03,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:04,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:05,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:06,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:06,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:07,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:08,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:09,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:10,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:10,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:11,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:12,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:13,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:13,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:14,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:15,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:16,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:17,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:17,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:18,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:19,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:20,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:21,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:22,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:23,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:24,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:24,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:25,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:26,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:28,060][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:52:28,987][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:52:28,989][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:52:30,296][__main__][INFO] - Iteration 690 took 54s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 32m 21s. Estimated total time: 15h 14m 20s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 26s, 500 more iterations: 7h 37m 10s. +[2025-08-20 18:52:30,297][__main__][INFO] - Starting iteration 690. +[2025-08-20 18:52:53,486][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:53,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:53,494][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:55,951][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:55,952][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:55,958][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:52:55,960][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:52:55,961][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:52:56,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:57,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:57,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:58,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:52:59,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:00,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:01,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:01,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:02,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:03,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:04,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:04,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:05,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:06,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:07,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:08,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:08,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:09,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:10,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:11,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:12,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:12,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:14,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:15,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:15,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:16,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:17,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:18,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:19,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:19,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:20,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:21,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:23,075][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:53:24,016][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:53:24,018][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:53:28,158][__main__][INFO] - Iteration 691 took 57s (35.86% Gen, 64.14% Train). Generation: 20s, Training: 37s. Estimated remaining time: 5h 21m 23s. Estimated total time: 16h 4m 20s. Time estimates for 10 more iterations: 9m 38s, 100 more iterations: 1h 36m 26s, 500 more iterations: 8h 2m 10s. +[2025-08-20 18:53:28,159][__main__][INFO] - Starting iteration 691. +[2025-08-20 18:53:51,470][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:53:51,471][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:53:51,478][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:53:53,947][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:53:53,948][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:53:53,954][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:53:53,956][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:53:53,957][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:53:54,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:55,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:55,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:56,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:57,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:58,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:59,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:53:59,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:00,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:01,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:02,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:02,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:03,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:04,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:05,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:06,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:06,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:07,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:08,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:09,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:10,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:10,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:11,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:12,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:13,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:14,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:15,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:16,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:16,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:17,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:18,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:19,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:21,004][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:54:21,926][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:54:21,928][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:54:23,242][__main__][INFO] - Iteration 692 took 55s (37.86% Gen, 62.14% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 34m 9s. Estimated total time: 15h 18m 1s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 0s. +[2025-08-20 18:54:23,243][__main__][INFO] - Starting iteration 692. +[2025-08-20 18:54:46,487][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:54:46,488][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:54:46,494][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:54:48,931][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:54:48,932][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:54:48,939][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:54:48,940][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:54:48,941][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:54:49,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:50,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:50,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:51,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:52,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:53,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:54,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:54,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:55,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:56,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:57,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:57,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:58,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:54:59,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:00,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:01,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:01,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:02,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:04,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:04,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:05,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:06,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:07,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:08,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:08,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:09,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:10,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:11,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:12,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:12,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:13,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:14,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:16,047][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:55:16,963][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:55:16,964][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:55:18,292][__main__][INFO] - Iteration 693 took 55s (37.80% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 32m 39s. Estimated total time: 15h 17m 26s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 44s, 500 more iterations: 7h 38m 43s. +[2025-08-20 18:55:18,293][__main__][INFO] - Starting iteration 693. +[2025-08-20 18:55:41,775][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:55:41,776][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:55:41,783][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:55:44,240][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:55:44,241][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:55:44,247][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:55:44,249][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:55:44,250][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:55:44,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:45,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:46,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:46,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:47,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:48,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:49,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:50,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:50,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:51,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:52,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:53,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:54,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:54,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:55,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:56,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:57,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:58,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:58,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:55:59,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:00,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:01,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:02,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:02,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:03,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:05,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:05,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:06,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:07,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:08,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:09,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:09,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:11,419][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:56:12,429][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:56:12,431][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:56:13,706][__main__][INFO] - Iteration 694 took 55s (37.94% Gen, 62.06% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 37m 49s. Estimated total time: 15h 23m 32s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 21s, 500 more iterations: 7h 41m 46s. +[2025-08-20 18:56:13,707][__main__][INFO] - Starting iteration 694. +[2025-08-20 18:56:36,711][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:56:36,712][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:56:36,719][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:56:39,170][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:56:39,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:56:39,177][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:56:39,179][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:56:39,180][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:56:39,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:40,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:41,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:41,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:42,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:43,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:44,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:45,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:45,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:46,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:47,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:48,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:49,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:49,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:50,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:51,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:52,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:52,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:53,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:54,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:55,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:56,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:56,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:57,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:58,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:56:59,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:00,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:01,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:02,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:02,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:03,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:04,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:06,166][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:57:07,102][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:57:07,103][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:57:08,386][__main__][INFO] - Iteration 695 took 54s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 24m 41s. Estimated total time: 15h 11m 18s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 7s, 500 more iterations: 7h 35m 39s. +[2025-08-20 18:57:08,388][__main__][INFO] - Starting iteration 695. +[2025-08-20 18:57:31,581][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:57:31,582][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:57:31,588][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:57:34,063][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:57:34,064][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:57:34,070][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:57:34,073][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:57:34,073][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:57:34,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:35,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:35,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:36,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:37,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:38,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:39,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:39,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:40,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:41,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:42,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:43,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:43,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:44,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:45,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:46,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:47,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:47,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:48,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:49,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:50,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:51,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:52,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:53,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:53,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:54,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:55,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:56,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:57,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:57,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:58,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:57:59,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:01,207][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:58:02,155][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:58:02,157][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:58:03,479][__main__][INFO] - Iteration 696 took 55s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 30m 38s. Estimated total time: 15h 18m 10s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 5s. +[2025-08-20 18:58:03,481][__main__][INFO] - Starting iteration 696. +[2025-08-20 18:58:27,258][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:58:27,260][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:58:27,266][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:58:29,699][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:58:29,700][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:58:29,707][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:58:29,709][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:58:29,710][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:58:30,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:30,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:31,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:32,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:33,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:33,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:34,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:35,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:36,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:37,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:37,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:38,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:39,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:40,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:41,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:41,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:42,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:43,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:44,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:45,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:45,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:46,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:47,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:48,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:49,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:50,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:51,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:51,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:52,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:53,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:54,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:55,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:58:56,689][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:58:57,675][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:58:57,676][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:58:59,000][__main__][INFO] - Iteration 697 took 55s (38.42% Gen, 61.57% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 36m 49s. Estimated total time: 15h 25m 17s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 31s, 500 more iterations: 7h 42m 38s. +[2025-08-20 18:58:59,002][__main__][INFO] - Starting iteration 697. +[2025-08-20 18:59:22,075][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:59:22,076][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:59:22,083][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:59:24,520][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:59:24,521][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:59:24,527][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 18:59:24,529][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 18:59:24,530][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 18:59:24,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:25,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:26,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:27,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:28,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:28,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:29,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:30,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:31,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:31,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:32,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:33,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:34,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:35,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:35,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:36,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:37,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:38,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:39,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:39,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:40,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:41,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:42,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:43,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:44,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:45,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:45,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:46,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:47,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:48,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:49,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:49,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 18:59:51,618][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 18:59:52,574][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 18:59:52,576][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 18:59:53,929][__main__][INFO] - Iteration 698 took 54s (37.57% Gen, 62.43% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 26m 4s. Estimated total time: 15h 15m 27s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 43s. +[2025-08-20 18:59:53,931][__main__][INFO] - Starting iteration 698. +[2025-08-20 19:00:17,061][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:00:17,062][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:00:17,069][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:00:19,525][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:00:19,526][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:00:19,533][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:00:19,535][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:00:19,536][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:00:19,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:20,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:21,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:22,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:23,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:23,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:24,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:25,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:26,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:26,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:27,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:28,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:29,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:30,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:30,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:31,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:32,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:33,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:34,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:34,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:35,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:36,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:37,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:38,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:39,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:40,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:41,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:41,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:42,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:43,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:44,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:45,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:00:46,684][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:00:47,659][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:00:47,660][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:00:48,958][__main__][INFO] - Iteration 699 took 55s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 26m 49s. Estimated total time: 15h 17m 7s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 33s. +[2025-08-20 19:00:48,960][__main__][INFO] - Starting iteration 699. +[2025-08-20 19:01:12,008][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:01:12,010][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:01:12,016][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:01:14,470][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:01:14,471][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:01:14,478][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:01:14,480][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:01:14,481][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:01:14,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:15,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:16,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:17,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:17,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:18,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:19,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:20,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:21,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:21,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:22,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:23,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:24,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:25,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:25,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:26,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:27,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:28,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:29,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:29,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:30,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:31,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:32,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:33,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:34,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:35,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:35,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:36,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:37,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:38,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:39,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:39,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:01:41,510][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:01:42,485][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:01:42,486][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:01:43,836][__main__][INFO] - Iteration 700 took 54s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 23m 23s. Estimated total time: 15h 14m 36s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 18s. +[2025-08-20 19:01:43,839][__main__][INFO] - Starting iteration 700. +[2025-08-20 19:02:07,020][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:02:07,022][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:02:07,028][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:02:09,495][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:02:09,496][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:02:09,503][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:02:09,505][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:02:09,506][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:02:09,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:10,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:11,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:12,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:12,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:13,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:14,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:15,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:16,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:16,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:17,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:18,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:19,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:20,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:20,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:21,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:22,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:23,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:24,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:24,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:25,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:26,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:27,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:28,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:29,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:30,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:31,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:31,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:32,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:33,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:34,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:35,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:02:36,638][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:02:37,603][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:02:37,604][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:02:41,939][__main__][INFO] - Iteration 701 took 58s (35.64% Gen, 59.12% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5h 16m 9s. Estimated total time: 16h 8m 20s. Time estimates for 10 more iterations: 9m 41s, 100 more iterations: 1h 36m 50s, 500 more iterations: 8h 4m 10s. +[2025-08-20 19:02:41,941][__main__][INFO] - Starting iteration 701. +[2025-08-20 19:03:05,290][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:03:05,291][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:03:05,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:03:07,729][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:03:07,730][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:03:07,737][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:03:07,739][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:03:07,739][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:03:08,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:08,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:09,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:10,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:11,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:11,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:12,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:13,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:14,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:15,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:15,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:16,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:17,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:18,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:19,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:19,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:20,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:21,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:22,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:23,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:24,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:25,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:26,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:26,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:27,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:28,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:29,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:30,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:30,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:31,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:32,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:33,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:03:34,799][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:03:35,798][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:03:35,799][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:03:37,112][__main__][INFO] - Iteration 702 took 55s (37.92% Gen, 62.08% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 26m 24s. Estimated total time: 15h 19m 30s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 45s. +[2025-08-20 19:03:37,114][__main__][INFO] - Starting iteration 702. +[2025-08-20 19:04:00,406][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:00,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:00,414][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:02,857][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:02,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:02,865][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:02,867][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:04:02,867][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:04:03,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:03,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:04,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:05,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:06,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:07,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:07,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:08,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:09,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:10,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:11,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:11,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:12,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:13,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:14,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:15,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:16,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:17,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:17,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:18,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:19,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:20,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:21,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:21,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:22,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:23,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:24,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:25,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:25,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:26,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:27,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:28,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:29,905][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:04:30,863][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:04:30,865][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:04:32,132][__main__][INFO] - Iteration 703 took 55s (37.92% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 22m 56s. Estimated total time: 15h 16m 57s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 28s. +[2025-08-20 19:04:32,134][__main__][INFO] - Starting iteration 703. +[2025-08-20 19:04:55,265][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:55,266][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:55,273][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:57,724][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:57,725][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:57,731][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:04:57,733][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:04:57,734][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:04:58,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:58,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:04:59,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:00,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:01,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:02,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:02,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:03,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:04,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:05,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:05,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:06,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:07,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:08,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:09,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:09,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:10,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:11,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:12,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:13,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:13,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:14,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:15,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:16,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:17,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:18,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:19,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:20,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:20,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:21,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:22,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:23,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:24,898][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:05:25,834][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:05:25,836][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:05:28,209][__main__][INFO] - Iteration 704 took 56s (36.89% Gen, 63.11% Train). Generation: 20s, Training: 35s. Estimated remaining time: 4h 39m 38s. Estimated total time: 15h 34m 35s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 27s, 500 more iterations: 7h 47m 17s. +[2025-08-20 19:05:28,211][__main__][INFO] - Starting iteration 704. +[2025-08-20 19:05:51,238][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:05:51,240][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:05:51,246][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:05:53,708][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:05:53,709][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:05:53,716][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:05:53,718][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:05:53,719][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:05:54,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:54,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:55,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:56,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:57,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:57,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:05:58,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:00,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:01,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:02,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:03,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:04,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:04,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:05,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:06,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:07,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:07,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:08,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:09,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:10,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:11,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:12,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:13,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:14,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:14,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:15,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:16,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:17,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:18,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:18,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:19,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:20,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:22,034][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:06:22,944][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:06:22,946][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:06:24,291][__main__][INFO] - Iteration 705 took 56s (36.68% Gen, 63.32% Train). Generation: 20s, Training: 35s. Estimated remaining time: 4h 38m 46s. Estimated total time: 15h 34m 39s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 27s, 500 more iterations: 7h 47m 19s. +[2025-08-20 19:06:24,292][__main__][INFO] - Starting iteration 705. +[2025-08-20 19:06:47,553][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:06:47,554][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:06:47,560][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:06:50,028][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:06:50,029][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:06:50,036][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:06:50,038][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:06:50,038][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:06:50,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:51,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:51,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:52,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:53,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:54,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:55,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:55,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:56,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:57,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:58,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:59,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:06:59,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:00,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:01,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:02,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:03,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:03,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:04,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:05,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:06,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:07,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:08,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:09,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:09,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:10,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:11,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:12,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:13,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:13,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:14,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:15,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:17,062][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:07:18,058][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:07:18,060][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:07:29,533][__main__][INFO] - Iteration 706 took 1m 5s (31.89% Gen, 68.11% Train). Generation: 20s, Training: 44s. Estimated remaining time: 7h 10m 21s. Estimated total time: 18h 7m 19s. Time estimates for 10 more iterations: 10m 52s, 100 more iterations: 1h 48m 43s, 500 more iterations: 9h 3m 39s. +[2025-08-20 19:07:29,534][__main__][INFO] - Starting iteration 706. +[2025-08-20 19:07:52,985][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:07:52,987][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:07:52,993][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:07:55,453][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:07:55,455][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:07:55,462][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:07:55,464][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:07:55,465][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:07:55,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:56,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:57,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:58,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:58,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:07:59,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:00,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:01,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:02,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:02,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:03,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:04,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:05,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:06,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:07,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:08,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:08,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:09,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:10,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:11,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:12,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:12,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:13,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:14,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:15,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:16,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:16,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:17,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:18,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:19,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:19,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:20,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:22,421][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:08:23,332][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:08:23,333][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:08:24,796][__main__][INFO] - Iteration 707 took 55s (37.97% Gen, 62.03% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 23m 7s. Estimated total time: 15h 21m 0s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 30s. +[2025-08-20 19:08:24,797][__main__][INFO] - Starting iteration 707. +[2025-08-20 19:08:48,997][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:08:48,998][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:08:49,004][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:08:51,466][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:08:51,467][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:08:51,474][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:08:51,476][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:08:51,477][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:08:51,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:52,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:53,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:54,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:54,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:55,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:56,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:57,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:58,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:58,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:08:59,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:00,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:01,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:02,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:02,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:03,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:04,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:05,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:06,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:06,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:07,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:08,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:09,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:10,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:11,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:12,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:12,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:13,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:14,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:15,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:16,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:16,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:18,405][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:09:19,314][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:09:19,315][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:09:20,785][__main__][INFO] - Iteration 708 took 55s (38.84% Gen, 61.16% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 34m 18s. Estimated total time: 15h 33m 7s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 18s, 500 more iterations: 7h 46m 33s. +[2025-08-20 19:09:20,787][__main__][INFO] - Starting iteration 708. +[2025-08-20 19:09:44,065][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:09:44,066][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:09:44,073][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:09:46,550][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:09:46,551][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:09:46,558][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:09:46,560][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:09:46,560][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:09:46,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:47,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:48,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:49,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:50,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:50,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:51,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:52,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:53,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:54,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:54,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:55,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:56,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:57,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:57,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:58,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:09:59,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:00,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:01,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:02,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:03,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:04,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:04,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:05,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:06,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:07,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:07,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:08,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:09,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:10,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:11,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:11,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:13,565][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:10:14,529][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:10:14,531][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:10:15,840][__main__][INFO] - Iteration 709 took 55s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 17m 48s. Estimated total time: 15h 17m 33s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 46s. +[2025-08-20 19:10:15,842][__main__][INFO] - Starting iteration 709. +[2025-08-20 19:10:39,034][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:10:39,035][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:10:39,041][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:10:41,503][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:10:41,504][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:10:41,512][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:10:41,514][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:10:41,515][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:10:41,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:42,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:43,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:44,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:44,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:45,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:46,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:47,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:48,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:48,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:49,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:50,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:51,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:52,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:52,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:53,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:54,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:55,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:56,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:56,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:58,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:58,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:10:59,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:00,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:01,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:02,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:02,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:03,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:04,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:05,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:06,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:06,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:08,514][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:11:09,438][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:11:09,439][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:11:10,844][__main__][INFO] - Iteration 710 took 55s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 16m 1s. Estimated total time: 15h 16m 41s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 20s. +[2025-08-20 19:11:10,845][__main__][INFO] - Starting iteration 710. +[2025-08-20 19:11:34,641][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:11:34,643][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:11:34,649][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:11:37,070][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:11:37,072][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:11:37,078][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:11:37,080][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:11:37,081][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:11:37,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:38,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:38,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:39,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:40,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:41,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:42,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:42,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:43,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:44,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:45,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:46,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:46,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:47,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:48,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:49,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:50,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:50,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:51,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:52,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:53,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:54,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:55,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:56,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:56,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:57,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:58,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:11:59,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:00,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:00,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:01,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:02,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:04,019][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:12:04,995][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:12:04,996][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:12:06,317][__main__][INFO] - Iteration 711 took 55s (38.52% Gen, 61.48% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 22m 56s. Estimated total time: 15h 24m 31s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 27s, 500 more iterations: 7h 42m 15s. +[2025-08-20 19:12:06,319][__main__][INFO] - Starting iteration 711. +[2025-08-20 19:12:30,039][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:12:30,040][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:12:30,046][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:12:32,523][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:12:32,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:12:32,533][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:12:32,535][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:12:32,536][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:12:32,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:33,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:34,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:35,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:36,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:36,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:37,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:38,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:39,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:39,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:40,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:41,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:42,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:43,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:43,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:44,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:45,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:46,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:47,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:47,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:48,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:49,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:50,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:51,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:51,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:52,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:54,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:54,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:55,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:56,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:57,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:57,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:12:59,551][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:13:00,483][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:13:00,484][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:13:01,762][__main__][INFO] - Iteration 712 took 55s (38.28% Gen, 61.72% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 21m 32s. Estimated total time: 15h 24m 2s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 24s, 500 more iterations: 7h 42m 1s. +[2025-08-20 19:13:01,764][__main__][INFO] - Starting iteration 712. +[2025-08-20 19:13:25,199][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:13:25,200][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:13:25,206][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:13:27,653][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:13:27,654][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:13:27,661][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:13:27,663][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:13:27,664][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:13:27,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:28,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:29,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:30,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:31,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:31,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:32,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:33,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:34,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:35,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:35,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:36,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:37,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:38,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:39,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:39,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:40,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:41,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:42,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:43,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:43,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:44,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:45,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:46,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:47,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:48,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:49,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:49,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:50,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:51,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:52,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:53,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:13:54,669][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:13:55,587][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:13:55,589][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:13:57,222][__main__][INFO] - Iteration 713 took 55s (37.84% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 20m 51s. Estimated total time: 15h 24m 17s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 25s, 500 more iterations: 7h 42m 8s. +[2025-08-20 19:13:57,223][__main__][INFO] - Starting iteration 713. +[2025-08-20 19:14:20,425][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:14:20,427][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:14:20,433][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:14:22,888][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:14:22,889][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:14:22,896][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:14:22,898][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:14:22,898][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:14:23,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:23,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:24,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:25,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:26,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:27,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:27,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:28,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:29,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:30,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:31,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:31,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:32,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:33,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:34,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:35,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:35,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:36,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:37,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:38,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:39,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:40,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:41,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:41,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:42,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:43,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:44,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:45,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:45,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:46,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:47,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:48,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:14:49,895][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:14:50,878][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:14:50,880][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:14:52,442][__main__][INFO] - Iteration 714 took 55s (37.59% Gen, 62.41% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 15m 57s. Estimated total time: 15h 20m 18s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 1s, 500 more iterations: 7h 40m 9s. +[2025-08-20 19:14:52,444][__main__][INFO] - Starting iteration 714. +[2025-08-20 19:15:15,932][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:15:15,933][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:15:15,940][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:15:18,405][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:15:18,407][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:15:18,413][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:15:18,415][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:15:18,416][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:15:18,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:19,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:20,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:21,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:21,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:22,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:23,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:24,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:25,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:25,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:26,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:27,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:28,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:29,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:29,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:30,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:31,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:32,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:33,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:33,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:34,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:35,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:36,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:36,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:37,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:39,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:39,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:41,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:42,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:42,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:43,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:44,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:15:46,076][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:15:46,957][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:15:46,958][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:15:48,273][__main__][INFO] - Iteration 715 took 55s (37.69% Gen, 62.31% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 25m 11s. Estimated total time: 15h 30m 28s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 2s, 500 more iterations: 7h 45m 14s. +[2025-08-20 19:15:48,274][__main__][INFO] - Starting iteration 715. +[2025-08-20 19:16:11,399][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:16:11,400][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:16:11,407][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:16:13,865][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:16:13,866][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:16:13,873][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:16:13,875][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:16:13,876][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:16:14,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:14,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:15,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:16,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:17,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:18,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:18,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:19,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:20,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:21,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:22,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:22,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:23,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:24,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:25,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:26,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:26,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:27,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:28,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:29,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:30,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:31,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:32,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:32,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:33,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:34,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:35,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:36,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:36,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:37,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:38,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:39,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:16:40,780][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:16:41,727][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:16:41,728][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:16:43,029][__main__][INFO] - Iteration 716 took 54s (37.76% Gen, 62.24% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 6m 21s. Estimated total time: 15h 12m 33s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 16s. +[2025-08-20 19:16:43,030][__main__][INFO] - Starting iteration 716. +[2025-08-20 19:17:06,329][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:17:06,330][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:17:06,337][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:17:08,825][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:17:08,827][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:17:08,833][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:17:08,835][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:17:08,836][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:17:09,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:09,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:10,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:11,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:12,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:13,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:13,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:14,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:15,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:16,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:17,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:17,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:18,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:19,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:20,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:21,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:21,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:22,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:23,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:24,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:25,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:26,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:27,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:27,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:28,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:29,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:30,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:31,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:31,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:32,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:33,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:34,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:17:35,820][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:17:36,740][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:17:36,742][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:17:38,100][__main__][INFO] - Iteration 717 took 55s (37.81% Gen, 62.19% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 10m 42s. Estimated total time: 15h 17m 49s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 46s, 500 more iterations: 7h 38m 54s. +[2025-08-20 19:17:38,101][__main__][INFO] - Starting iteration 717. +[2025-08-20 19:18:01,575][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:01,576][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:01,582][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:04,051][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:04,052][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:04,059][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:04,061][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:18:04,062][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:18:04,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:05,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:05,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:06,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:07,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:08,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:09,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:09,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:10,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:11,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:12,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:13,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:13,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:14,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:15,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:16,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:17,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:17,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:18,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:19,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:20,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:21,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:21,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:22,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:23,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:24,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:25,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:26,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:27,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:27,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:28,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:29,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:18:31,096][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:18:32,054][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:18:32,056][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:18:33,328][__main__][INFO] - Iteration 718 took 55s (38.08% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 12m 24s. Estimated total time: 15h 20m 26s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 13s. +[2025-08-20 19:18:33,330][__main__][INFO] - Starting iteration 718. +[2025-08-20 19:18:56,633][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:56,635][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:56,641][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:59,111][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:59,113][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:59,119][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:18:59,122][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:18:59,122][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:18:59,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:00,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:01,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:01,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:02,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:03,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:04,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:04,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:05,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:06,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:07,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:08,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:08,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:09,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:10,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:11,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:12,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:12,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:13,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:14,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:15,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:16,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:16,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:18,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:18,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:19,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:20,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:21,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:22,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:22,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:23,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:24,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:26,041][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:19:26,940][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:19:26,941][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:19:28,269][__main__][INFO] - Iteration 719 took 54s (37.95% Gen, 62.04% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 6m 41s. Estimated total time: 15h 15m 38s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 49s. +[2025-08-20 19:19:28,270][__main__][INFO] - Starting iteration 719. +[2025-08-20 19:19:51,531][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:19:51,532][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:19:51,539][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:19:53,979][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:19:53,981][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:19:53,987][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:19:53,989][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:19:53,990][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:19:54,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:55,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:55,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:56,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:57,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:58,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:59,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:19:59,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:00,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:01,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:02,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:03,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:03,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:04,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:05,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:06,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:06,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:07,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:08,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:09,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:10,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:10,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:11,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:12,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:13,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:14,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:15,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:16,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:16,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:17,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:18,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:19,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:20,963][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:20:21,957][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:20:21,958][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:20:23,309][__main__][INFO] - Iteration 720 took 55s (37.85% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 7m 26s. Estimated total time: 15h 17m 18s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 39s. +[2025-08-20 19:20:23,311][__main__][INFO] - Starting iteration 720. +[2025-08-20 19:20:46,525][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:20:46,526][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:20:46,532][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:20:49,000][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:20:49,001][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:20:49,008][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:20:49,010][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:20:49,011][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:20:49,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:50,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:50,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:51,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:52,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:53,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:54,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:54,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:55,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:56,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:57,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:58,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:58,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:20:59,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:00,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:01,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:02,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:02,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:03,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:04,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:05,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:05,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:07,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:08,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:08,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:09,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:10,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:11,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:12,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:12,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:13,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:14,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:21:16,019][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:21:16,993][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:21:16,995][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:21:18,297][__main__][INFO] - Iteration 721 took 54s (37.72% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 5m 39s. Estimated total time: 15h 16m 26s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 13s. +[2025-08-20 19:21:18,299][__main__][INFO] - Starting iteration 721. +[2025-08-20 19:21:42,193][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:21:42,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:21:42,201][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:21:44,637][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:21:44,639][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:21:44,645][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:21:44,647][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:21:44,648][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:21:44,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:45,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:46,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:47,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:48,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:48,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:49,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:50,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:51,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:52,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:52,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:53,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:54,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:55,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:56,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:56,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:57,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:58,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:21:59,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:00,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:00,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:01,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:02,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:03,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:04,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:05,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:06,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:06,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:07,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:08,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:09,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:09,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8961 tokens. +[2025-08-20 19:22:11,609][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:22:12,539][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:22:12,541][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:22:13,970][__main__][INFO] - Iteration 722 took 55s (38.52% Gen, 61.47% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 16m 7s. Estimated total time: 15h 27m 50s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 47s, 500 more iterations: 7h 43m 55s. +[2025-08-20 19:22:13,971][__main__][INFO] - Starting iteration 722. +[2025-08-20 19:22:37,536][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:22:37,537][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:22:37,543][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:22:40,002][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:22:40,003][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:22:40,010][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:22:40,012][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:22:40,012][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:22:40,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:41,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:41,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:42,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:43,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:44,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:45,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:45,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:46,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:47,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:48,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:49,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:49,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:50,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:51,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:52,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:53,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:53,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:55,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:55,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:56,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:57,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:58,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:59,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:22:59,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:00,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:01,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:02,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:03,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:04,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:04,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:05,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:07,174][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:23:08,114][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:23:08,115][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:23:09,394][__main__][INFO] - Iteration 723 took 55s (38.08% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 11m 4s. Estimated total time: 15h 23m 42s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 22s, 500 more iterations: 7h 41m 51s. +[2025-08-20 19:23:09,395][__main__][INFO] - Starting iteration 723. +[2025-08-20 19:23:32,631][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:23:32,633][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:23:32,639][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:23:35,109][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:23:35,110][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:23:35,116][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:23:35,119][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:23:35,119][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:23:35,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:36,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:37,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:37,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:38,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:39,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:40,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:40,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:41,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:42,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:43,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:44,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:44,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:45,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:46,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:47,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:48,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:49,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:50,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:51,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:51,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:52,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:53,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:54,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:54,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:55,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:56,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:57,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:58,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:58,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:23:59,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:00,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:02,237][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:24:03,185][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:24:03,187][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:24:04,499][__main__][INFO] - Iteration 724 took 55s (37.71% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 4m 49s. Estimated total time: 15h 18m 23s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 50s, 500 more iterations: 7h 39m 11s. +[2025-08-20 19:24:04,502][__main__][INFO] - Starting iteration 724. +[2025-08-20 19:24:27,760][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:24:27,761][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:24:27,767][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:24:30,210][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:24:30,211][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:24:30,218][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:24:30,220][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:24:30,221][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:24:30,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:31,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:32,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:32,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:33,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:34,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:35,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:36,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:36,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:37,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:38,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:39,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:40,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:40,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:41,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:42,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:43,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:43,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:44,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:45,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:46,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:47,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:48,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:49,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:50,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:50,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:51,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:52,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:53,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:54,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:54,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:55,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:24:57,238][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:24:58,208][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:24:58,210][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:24:59,497][__main__][INFO] - Iteration 725 took 54s (37.84% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 2m 3s. Estimated total time: 15h 16m 31s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 39s, 500 more iterations: 7h 38m 15s. +[2025-08-20 19:24:59,498][__main__][INFO] - Starting iteration 725. +[2025-08-20 19:25:23,173][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:25:23,174][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:25:23,180][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:25:25,646][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:25:25,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:25:25,653][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:25:25,656][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:25:25,656][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:25:25,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:26,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:27,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:28,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:29,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:29,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:30,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:31,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:32,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:33,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:33,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:34,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:35,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:36,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:37,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:38,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:39,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:39,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:40,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:41,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:42,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:43,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:43,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:44,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:45,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:46,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:47,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:47,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:48,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:49,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:50,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:51,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:25:52,568][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:25:53,536][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:25:53,537][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:25:54,886][__main__][INFO] - Iteration 726 took 55s (38.30% Gen, 61.70% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 7m 43s. Estimated total time: 15h 23m 7s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 18s, 500 more iterations: 7h 41m 33s. +[2025-08-20 19:25:54,887][__main__][INFO] - Starting iteration 726. +[2025-08-20 19:26:18,299][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:26:18,301][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:26:18,307][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:26:20,783][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:26:20,785][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:26:20,791][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:26:20,793][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:26:20,794][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:26:21,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:21,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:22,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:23,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:24,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:25,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:25,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:26,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:27,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:28,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:29,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:29,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:30,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:31,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:32,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:32,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:33,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:34,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:35,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:36,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:37,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:38,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:39,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:39,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:40,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:41,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:42,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:43,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:43,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:44,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:45,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:46,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:26:47,800][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:26:48,768][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:26:48,769][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:26:50,042][__main__][INFO] - Iteration 727 took 55s (37.99% Gen, 62.01% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 2m 55s. Estimated total time: 15h 19m 14s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 37s. +[2025-08-20 19:26:50,043][__main__][INFO] - Starting iteration 727. +[2025-08-20 19:27:13,998][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:27:13,999][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:27:14,005][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:27:16,435][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:27:16,436][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:27:16,442][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:27:16,444][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:27:16,445][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:27:16,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:17,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:18,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:19,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:19,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:20,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:21,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:22,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:23,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:23,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:24,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:25,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:26,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:27,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:27,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:28,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:29,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:30,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:31,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:31,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:32,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:33,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:34,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:35,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:36,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:37,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:37,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:38,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:39,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:40,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:41,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:41,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:27:43,373][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:27:44,368][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:27:44,370][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:27:45,696][__main__][INFO] - Iteration 728 took 55s (38.66% Gen, 61.34% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 10m 17s. Estimated total time: 15h 27m 32s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 45s, 500 more iterations: 7h 43m 46s. +[2025-08-20 19:27:45,697][__main__][INFO] - Starting iteration 728. +[2025-08-20 19:28:08,948][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:28:08,949][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:28:08,956][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:28:11,413][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:28:11,415][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:28:11,421][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:28:11,423][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:28:11,424][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:28:11,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:12,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:13,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:14,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:14,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:15,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:16,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:17,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:18,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:18,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:19,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:20,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:21,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:22,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:22,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:23,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:24,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:25,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:26,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:26,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:28,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:28,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:29,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:30,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:31,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:32,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:32,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:33,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:34,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:35,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:36,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:36,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:28:38,402][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:28:39,306][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:28:39,308][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:28:40,609][__main__][INFO] - Iteration 729 took 54s (37.87% Gen, 62.13% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 57m 1s. Estimated total time: 15h 15m 11s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 31s, 500 more iterations: 7h 37m 35s. +[2025-08-20 19:28:40,611][__main__][INFO] - Starting iteration 729. +[2025-08-20 19:29:03,908][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:29:03,910][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:29:03,917][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:29:06,383][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:29:06,384][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:29:06,390][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:29:06,393][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:29:06,393][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:29:06,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:07,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:08,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:09,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:09,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:10,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:11,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:12,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:13,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:13,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:14,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:15,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:16,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:17,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:17,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:18,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:19,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:20,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:20,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:21,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:23,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:23,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:24,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:25,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:26,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:27,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:27,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:28,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:29,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:30,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:31,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:31,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:29:33,446][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:29:34,463][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:29:34,465][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:29:35,732][__main__][INFO] - Iteration 730 took 55s (37.80% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 59m 36s. Estimated total time: 15h 18m 40s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 20s. +[2025-08-20 19:29:35,734][__main__][INFO] - Starting iteration 730. +[2025-08-20 19:30:00,197][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:00,199][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:00,205][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:02,665][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:02,667][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:02,673][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:02,675][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:30:02,675][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:30:02,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:03,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:04,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:05,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:06,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:06,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:07,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:08,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:09,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:10,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:10,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:11,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:12,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:13,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:14,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:14,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:15,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:16,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:17,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:18,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:19,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:20,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:20,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:21,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:22,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:23,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:24,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:24,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:25,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:26,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:27,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:27,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:29,607][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:30:30,593][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:30:30,595][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:30:32,161][__main__][INFO] - Iteration 731 took 56s (38.99% Gen, 61.01% Train). Generation: 22s, Training: 34s. Estimated remaining time: 4h 20m 25s. Estimated total time: 15h 40m 27s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 2s, 500 more iterations: 7h 50m 13s. +[2025-08-20 19:30:32,163][__main__][INFO] - Starting iteration 731. +[2025-08-20 19:30:55,598][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:55,600][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:55,606][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:58,069][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:58,070][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:58,077][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:30:58,078][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:30:58,079][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:30:58,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:59,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:30:59,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:00,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:01,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:02,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:03,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:03,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:04,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:05,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:06,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:07,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:07,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:08,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:09,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:10,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:11,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:11,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:12,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:13,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:14,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:15,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:16,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:17,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:17,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:18,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:19,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:20,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:21,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:21,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:22,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:23,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:25,196][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:31:26,165][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:31:26,166][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:31:27,484][__main__][INFO] - Iteration 732 took 55s (37.93% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 1m 4s. Estimated total time: 15h 22m 0s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 12s, 500 more iterations: 7h 41m 0s. +[2025-08-20 19:31:27,485][__main__][INFO] - Starting iteration 732. +[2025-08-20 19:31:51,410][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:31:51,411][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:31:51,418][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:31:53,900][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:31:53,902][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:31:53,908][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:31:53,910][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:31:53,911][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:31:54,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:54,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:55,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:56,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:57,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:58,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:58,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:31:59,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:00,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:01,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:02,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:02,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:03,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:04,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:05,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:06,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:06,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:07,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:08,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:09,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:10,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:11,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:12,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:12,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:13,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:14,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:15,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:16,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:16,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:17,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:18,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:19,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:20,968][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:32:21,927][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:32:21,929][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:32:23,213][__main__][INFO] - Iteration 733 took 55s (38.52% Gen, 61.48% Train). Generation: 21s, Training: 34s. Estimated remaining time: 4h 6m 55s. Estimated total time: 15h 28m 47s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 52s, 500 more iterations: 7h 44m 23s. +[2025-08-20 19:32:23,214][__main__][INFO] - Starting iteration 733. +[2025-08-20 19:32:46,820][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:32:46,821][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:32:46,828][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:32:49,308][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:32:49,309][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:32:49,316][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:32:49,318][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:32:49,319][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:32:49,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:50,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:51,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:51,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:52,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:53,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:54,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:55,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:55,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:56,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:57,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:58,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:59,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:32:59,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:00,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:01,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:02,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:03,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:03,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:04,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:05,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:06,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:07,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:08,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:09,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:09,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:10,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:11,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:12,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:13,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:13,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:14,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:16,244][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:33:17,235][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:33:17,236][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:33:18,547][__main__][INFO] - Iteration 734 took 55s (38.20% Gen, 61.80% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 59m 24s. Estimated total time: 15h 22m 12s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 6s. +[2025-08-20 19:33:18,548][__main__][INFO] - Starting iteration 734. +[2025-08-20 19:33:41,738][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:33:41,740][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:33:41,746][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:33:44,187][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:33:44,188][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:33:44,194][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:33:44,196][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:33:44,197][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:33:44,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:45,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:46,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:46,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:47,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:48,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:49,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:50,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:50,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:51,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:52,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:53,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:54,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:54,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:55,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:56,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:57,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:57,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:58,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:33:59,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:00,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:01,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:01,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:02,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:04,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:04,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:05,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:06,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:07,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:08,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:08,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:09,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:11,371][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:34:12,311][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:34:12,313][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:34:13,582][__main__][INFO] - Iteration 735 took 55s (37.73% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 53m 30s. Estimated total time: 15h 17m 13s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 43s, 500 more iterations: 7h 38m 36s. +[2025-08-20 19:34:13,583][__main__][INFO] - Starting iteration 735. +[2025-08-20 19:34:36,894][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:34:36,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:34:36,901][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:34:39,332][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:34:39,333][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:34:39,339][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:34:39,342][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:34:39,342][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:34:39,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:40,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:41,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:42,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:42,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:43,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:44,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:45,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:45,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:46,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:47,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:48,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:49,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:49,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:50,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:51,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:52,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:53,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:53,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:54,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:55,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:56,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:57,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:58,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:59,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:34:59,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:00,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:01,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:02,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:03,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:03,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:04,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:06,408][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:35:07,334][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:35:07,336][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:35:08,765][__main__][INFO] - Iteration 736 took 55s (37.87% Gen, 62.13% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 55m 3s. Estimated total time: 15h 19m 40s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 58s, 500 more iterations: 7h 39m 50s. +[2025-08-20 19:35:08,766][__main__][INFO] - Starting iteration 736. +[2025-08-20 19:35:31,922][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:35:31,923][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:35:31,929][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:35:34,407][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:35:34,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:35:34,415][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:35:34,417][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:35:34,418][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:35:34,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:35,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:36,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:37,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:37,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:38,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:39,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:40,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:41,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:41,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:42,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:43,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:44,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:45,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:45,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:46,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:47,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:48,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:48,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:49,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:50,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:51,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:52,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:53,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:54,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:54,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:55,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:56,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:57,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:58,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:58,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:35:59,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:01,374][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:36:02,386][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:36:02,388][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:36:03,707][__main__][INFO] - Iteration 737 took 54s (37.68% Gen, 62.32% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 50m 7s. Estimated total time: 15h 15m 40s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 34s, 500 more iterations: 7h 37m 50s. +[2025-08-20 19:36:03,709][__main__][INFO] - Starting iteration 737. +[2025-08-20 19:36:27,258][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:36:27,259][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:36:27,265][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:36:29,722][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:36:29,723][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:36:29,729][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:36:29,731][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:36:29,732][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:36:30,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:30,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:31,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:32,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:33,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:33,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:34,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:35,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:36,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:37,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:37,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:38,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:39,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:40,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:41,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:41,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:42,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:43,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:44,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:45,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:45,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:46,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:47,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:48,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:49,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:50,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:51,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:52,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:52,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:53,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:54,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:55,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:36:56,798][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:36:57,703][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:36:57,704][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:36:59,101][__main__][INFO] - Iteration 738 took 55s (38.08% Gen, 61.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 56m 44s. Estimated total time: 15h 23m 12s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 36s. +[2025-08-20 19:36:59,103][__main__][INFO] - Starting iteration 738. +[2025-08-20 19:37:22,284][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:37:22,285][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:37:22,291][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:37:24,745][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:37:24,747][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:37:24,753][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:37:24,755][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:37:24,756][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:37:25,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:25,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:26,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:27,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:28,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:29,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:29,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:30,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:31,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:32,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:32,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:33,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:34,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:35,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:36,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:36,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:37,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:38,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:39,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:40,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:40,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:41,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:42,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:43,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:44,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:45,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:46,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:47,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:47,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:48,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:49,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:50,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:37:51,869][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:37:52,913][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:37:52,916][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:37:54,234][__main__][INFO] - Iteration 739 took 55s (37.62% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 51m 27s. Estimated total time: 15h 18m 50s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 53s, 500 more iterations: 7h 39m 25s. +[2025-08-20 19:37:54,235][__main__][INFO] - Starting iteration 739. +[2025-08-20 19:38:17,416][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:38:17,417][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:38:17,423][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:38:19,883][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:38:19,884][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:38:19,891][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:38:19,893][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:38:19,893][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:38:20,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:20,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:21,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:22,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:23,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:24,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:24,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:25,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:26,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:27,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:28,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:28,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:29,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:30,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:31,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:32,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:32,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:33,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:34,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:35,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:36,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:37,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:38,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:38,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:39,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:40,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:41,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:42,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:42,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:43,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:44,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:45,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:38:46,888][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:38:47,878][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:38:47,880][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:38:49,260][__main__][INFO] - Iteration 740 took 55s (37.66% Gen, 62.34% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 48m 46s. Estimated total time: 15h 17m 4s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 42s, 500 more iterations: 7h 38m 32s. +[2025-08-20 19:38:49,262][__main__][INFO] - Starting iteration 740. +[2025-08-20 19:39:12,527][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:39:12,528][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:39:12,534][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:39:14,983][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:39:14,985][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:39:14,991][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:39:14,993][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:39:14,994][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:39:15,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:16,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:16,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:17,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:18,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:19,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:20,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:20,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:21,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:22,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:23,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:24,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:24,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:25,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:26,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:27,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:28,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:28,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:29,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:30,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:31,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:31,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:32,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:34,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:34,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:35,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:36,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:37,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:38,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:38,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:39,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:40,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:39:42,120][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:39:42,993][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:39:42,995][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:39:44,429][__main__][INFO] - Iteration 741 took 55s (37.70% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 50m 13s. Estimated total time: 15h 19m 27s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 56s, 500 more iterations: 7h 39m 43s. +[2025-08-20 19:39:44,431][__main__][INFO] - Starting iteration 741. +[2025-08-20 19:40:07,551][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:40:07,552][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:40:07,559][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:40:10,030][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:40:10,031][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:40:10,038][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:40:10,040][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:40:10,041][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:40:10,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:11,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:11,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:12,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:13,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:14,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:15,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:15,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:16,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:17,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:18,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:19,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:19,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:20,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:21,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:22,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:23,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:23,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:25,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:25,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:26,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:27,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:28,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:29,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:29,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:30,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:31,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:32,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:33,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:33,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:34,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:35,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:40:37,105][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:40:38,019][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:40:38,020][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:40:39,272][__main__][INFO] - Iteration 742 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 43m 53s. Estimated total time: 15h 14m 1s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 0s. +[2025-08-20 19:40:39,274][__main__][INFO] - Starting iteration 742. +[2025-08-20 19:41:02,801][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:02,803][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:02,809][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:05,276][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:05,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:05,284][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:05,286][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:41:05,286][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:41:05,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:06,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:07,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:07,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:08,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:09,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:10,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:11,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:11,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:12,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:13,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:14,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:15,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:15,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:16,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:17,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:18,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:19,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:19,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:20,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:21,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:22,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:23,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:24,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:25,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:25,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:26,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:27,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:28,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:29,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:29,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:30,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:41:32,242][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:41:33,224][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:41:33,225][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:41:34,539][__main__][INFO] - Iteration 743 took 55s (38.11% Gen, 61.88% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 50m 1s. Estimated total time: 15h 21m 4s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 32s. +[2025-08-20 19:41:34,540][__main__][INFO] - Starting iteration 743. +[2025-08-20 19:41:57,732][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:57,734][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:41:57,740][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:00,207][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:00,209][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:00,215][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:00,217][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:42:00,218][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:42:00,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:01,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:02,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:02,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:03,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:04,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:05,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:06,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:06,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:07,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:08,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:09,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:10,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:10,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:11,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:12,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:13,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:14,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:14,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:15,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:16,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:17,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:17,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:19,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:20,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:20,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:21,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:22,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:23,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:24,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:24,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:25,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:27,256][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:42:28,195][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:42:28,196][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:42:29,588][__main__][INFO] - Iteration 744 took 55s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 45m 29s. Estimated total time: 15h 17m 27s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 44s, 500 more iterations: 7h 38m 43s. +[2025-08-20 19:42:29,590][__main__][INFO] - Starting iteration 744. +[2025-08-20 19:42:53,389][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:53,390][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:53,396][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:55,830][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:55,832][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:55,838][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:42:55,840][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:42:55,841][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:42:56,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:56,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:57,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:58,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:42:59,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:00,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:00,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:01,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:02,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:03,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:04,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:04,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:05,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:06,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:07,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:08,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:08,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:09,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:10,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:11,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:12,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:12,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:13,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:14,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:15,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:16,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:17,280][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:18,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:18,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:19,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:20,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:21,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:22,912][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:43:23,808][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:43:23,810][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:43:25,197][__main__][INFO] - Iteration 745 took 55s (38.42% Gen, 61.58% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 53m 52s. Estimated total time: 15h 26m 46s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 40s, 500 more iterations: 7h 43m 23s. +[2025-08-20 19:43:25,199][__main__][INFO] - Starting iteration 745. +[2025-08-20 19:43:48,880][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:43:48,882][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:43:48,888][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:43:51,359][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:43:51,361][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:43:51,367][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:43:51,369][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:43:51,370][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:43:51,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:52,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:53,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:54,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:54,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:55,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:56,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:57,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:58,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:58,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:43:59,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:00,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:01,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:01,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:02,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:03,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:04,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:05,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:05,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:06,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:07,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:08,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:09,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:10,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:11,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:11,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:12,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:13,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:14,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:15,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:15,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:16,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:18,329][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:44:19,337][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:44:19,339][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:44:20,659][__main__][INFO] - Iteration 746 took 55s (38.27% Gen, 61.73% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 50m 29s. Estimated total time: 15h 24m 19s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 25s, 500 more iterations: 7h 42m 9s. +[2025-08-20 19:44:20,660][__main__][INFO] - Starting iteration 746. +[2025-08-20 19:44:43,858][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:44:43,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:44:43,866][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:44:46,299][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:44:46,301][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:44:46,307][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:44:46,309][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:44:46,310][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:44:46,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:47,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:48,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:48,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:49,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:50,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:51,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:52,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:52,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:53,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:54,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:55,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:56,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:56,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:57,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:58,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:44:59,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:00,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:00,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:01,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:02,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:03,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:04,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:05,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:06,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:06,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:07,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:08,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:09,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:10,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:10,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:11,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:13,316][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:45:14,284][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:45:14,285][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:45:15,619][__main__][INFO] - Iteration 747 took 54s (37.76% Gen, 62.24% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 41m 14s. Estimated total time: 15h 15m 59s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 59s. +[2025-08-20 19:45:15,621][__main__][INFO] - Starting iteration 747. +[2025-08-20 19:45:39,455][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:45:39,457][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:45:39,463][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:45:41,918][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:45:41,920][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:45:41,926][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:45:41,928][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:45:41,929][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:45:42,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:43,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:43,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:44,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:45,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:46,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:46,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:47,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:48,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:49,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:50,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:50,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:51,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:52,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:53,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:54,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:54,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:55,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:56,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:57,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:58,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:45:59,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:00,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:00,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:01,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:02,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:03,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:04,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:04,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:05,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:06,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:07,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:08,994][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:46:10,077][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:46:10,079][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:46:11,507][__main__][INFO] - Iteration 748 took 55s (38.26% Gen, 61.74% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 55m 45s. Estimated total time: 15h 31m 26s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 8s, 500 more iterations: 7h 45m 43s. +[2025-08-20 19:46:11,509][__main__][INFO] - Starting iteration 748. +[2025-08-20 19:46:34,804][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:46:34,806][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:46:34,812][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:46:37,272][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:46:37,273][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:46:37,279][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:46:37,281][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:46:37,282][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:46:37,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:38,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:39,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:39,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:40,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:41,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:42,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:43,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:43,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:44,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:45,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:46,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:47,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:47,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:48,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:49,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:50,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:51,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:51,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:52,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:53,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:54,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:55,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:56,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:57,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:57,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:58,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:46:59,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:00,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:01,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:01,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:02,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:04,345][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:47:05,275][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:47:05,276][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:47:06,557][__main__][INFO] - Iteration 749 took 55s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 40m 52s. Estimated total time: 15h 17m 27s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 44s, 500 more iterations: 7h 38m 43s. +[2025-08-20 19:47:06,558][__main__][INFO] - Starting iteration 749. +[2025-08-20 19:47:30,258][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:47:30,260][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:47:30,266][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:47:32,756][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:47:32,758][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:47:32,764][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:47:32,766][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:47:32,766][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:47:33,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:33,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:34,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:35,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:36,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:37,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:37,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:38,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:39,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:40,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:41,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:41,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:42,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:43,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:44,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:44,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:45,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:46,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:47,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:48,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:48,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:49,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:50,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:51,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:52,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:52,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:54,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:55,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:55,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:56,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:57,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:58,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:47:59,854][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:48:00,819][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:48:00,821][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:48:02,077][__main__][INFO] - Iteration 750 took 55s (38.26% Gen, 61.74% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 47m 47s. Estimated total time: 15h 25m 18s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 31s, 500 more iterations: 7h 42m 39s. +[2025-08-20 19:48:02,078][__main__][INFO] - Starting iteration 750. +[2025-08-20 19:48:25,245][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:48:25,246][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:48:25,252][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:48:27,694][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:48:27,695][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:48:27,702][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:48:27,704][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:48:27,705][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:48:28,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:28,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:29,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:30,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:31,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:31,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:32,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:33,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:34,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:35,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:35,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:36,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:37,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:38,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:39,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:39,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:40,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:41,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:42,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:43,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:44,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:45,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:45,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:46,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:47,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:48,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:49,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:49,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:50,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:51,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:52,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:53,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:48:54,742][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:48:55,698][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:48:55,699][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:48:59,770][__main__][INFO] - Iteration 751 took 57s (35.93% Gen, 59.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4h 23m 3s. Estimated total time: 16h 1m 31s. Time estimates for 10 more iterations: 9m 36s, 100 more iterations: 1h 36m 9s, 500 more iterations: 8h 0m 45s. +[2025-08-20 19:48:59,772][__main__][INFO] - Starting iteration 751. +[2025-08-20 19:49:22,848][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:49:22,849][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:49:22,855][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:49:25,291][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:49:25,292][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:49:25,298][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:49:25,301][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:49:25,301][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:49:25,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:26,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:27,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:27,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:28,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:29,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:30,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:31,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:31,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:32,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:33,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:34,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:35,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:35,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:36,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:37,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:38,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:39,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:39,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:40,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:41,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:42,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:43,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:43,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:45,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:45,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:46,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:47,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:48,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:49,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:49,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:50,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:49:52,362][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:49:53,301][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:49:53,303][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:49:54,859][__main__][INFO] - Iteration 752 took 55s (37.46% Gen, 62.54% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 38m 42s. Estimated total time: 15h 18m 6s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 48s, 500 more iterations: 7h 39m 3s. +[2025-08-20 19:49:54,860][__main__][INFO] - Starting iteration 752. +[2025-08-20 19:50:18,241][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:50:18,242][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:50:18,248][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:50:20,716][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:50:20,718][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:50:20,724][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:50:20,726][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:50:20,727][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:50:21,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:21,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:22,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:23,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:24,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:24,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:25,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:26,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:27,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:28,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:28,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:29,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:30,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:31,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:32,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:32,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:33,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:34,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:35,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:36,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:36,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:38,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:38,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:39,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:40,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:41,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:42,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:42,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:43,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:44,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:45,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:46,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:50:47,800][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:50:48,802][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:50:48,805][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:50:50,038][__main__][INFO] - Iteration 753 took 55s (37.93% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 39m 17s. Estimated total time: 15h 19m 36s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 57s, 500 more iterations: 7h 39m 48s. +[2025-08-20 19:50:50,039][__main__][INFO] - Starting iteration 753. +[2025-08-20 19:51:13,107][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:51:13,109][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:51:13,115][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:51:15,567][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:51:15,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:51:15,575][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:51:15,577][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:51:15,578][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:51:15,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:16,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:17,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:18,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:19,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:19,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:20,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:21,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:22,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:23,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:23,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:24,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:25,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:26,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:26,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:27,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:28,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:29,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:30,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:30,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:31,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:32,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:33,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:34,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:34,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:35,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:37,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:37,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:38,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:39,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:40,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:41,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:51:42,661][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:51:43,587][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:51:43,588][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:51:44,880][__main__][INFO] - Iteration 754 took 54s (37.62% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 32m 46s. Estimated total time: 15h 14m 0s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 24s, 500 more iterations: 7h 37m 0s. +[2025-08-20 19:51:44,882][__main__][INFO] - Starting iteration 754. +[2025-08-20 19:52:07,892][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:52:07,894][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:52:07,900][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:52:10,344][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:52:10,345][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:52:10,351][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:52:10,353][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:52:10,354][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:52:10,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:11,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:12,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:13,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:13,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:14,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:15,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:16,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:16,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:17,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:18,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:19,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:20,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:20,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:21,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:22,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:23,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:24,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:25,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:26,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:27,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:27,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:28,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:29,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:30,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:30,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:31,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:32,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:33,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:34,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:34,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:35,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:52:37,396][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:52:38,340][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:52:38,342][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:52:39,646][__main__][INFO] - Iteration 755 took 54s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 30m 35s. Estimated total time: 15h 12m 44s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 16s, 500 more iterations: 7h 36m 22s. +[2025-08-20 19:52:39,648][__main__][INFO] - Starting iteration 755. +[2025-08-20 19:53:03,220][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:03,221][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:03,227][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:05,693][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:05,695][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:05,701][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:05,703][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:53:05,704][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:53:06,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:06,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:07,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:08,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:09,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:09,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:10,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:11,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:12,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:13,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:13,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:14,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:15,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:16,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:17,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:17,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:18,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:19,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:20,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:21,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:21,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:22,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:23,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:24,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:25,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:25,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:27,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:28,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:28,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:29,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:30,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:31,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:53:32,894][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:53:33,876][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:53:33,877][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:53:35,107][__main__][INFO] - Iteration 756 took 55s (38.06% Gen, 61.94% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 41m 15s. Estimated total time: 15h 24m 19s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 25s, 500 more iterations: 7h 42m 9s. +[2025-08-20 19:53:35,109][__main__][INFO] - Starting iteration 756. +[2025-08-20 19:53:58,176][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:58,177][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:53:58,183][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:00,619][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:00,620][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:00,626][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:00,629][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:54:00,629][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:54:00,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:01,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:02,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:03,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:04,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:04,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:05,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:06,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:07,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:08,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:08,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:09,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:10,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:11,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:12,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:12,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:13,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:14,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:15,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:16,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:16,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:18,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:18,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:19,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:20,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:21,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:22,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:22,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:23,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:24,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:25,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:26,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:27,700][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:54:28,611][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:54:28,613][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:54:29,948][__main__][INFO] - Iteration 757 took 54s (37.61% Gen, 62.38% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 29m 59s. Estimated total time: 15h 13m 58s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 23s, 500 more iterations: 7h 36m 59s. +[2025-08-20 19:54:29,950][__main__][INFO] - Starting iteration 757. +[2025-08-20 19:54:53,344][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:53,345][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:53,352][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:55,800][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:55,801][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:55,808][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:54:55,809][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:54:55,810][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:54:56,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:56,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:57,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:58,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:54:59,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:00,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:00,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:01,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:02,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:03,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:04,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:04,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:05,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:06,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:07,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:08,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:08,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:09,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:10,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:11,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:11,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:12,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:13,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:14,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:15,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:16,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:17,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:18,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:18,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:19,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:20,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:21,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:22,868][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:55:23,808][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:55:23,809][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:55:25,266][__main__][INFO] - Iteration 758 took 55s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 37m 1s. Estimated total time: 15h 21m 56s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 58s. +[2025-08-20 19:55:25,268][__main__][INFO] - Starting iteration 758. +[2025-08-20 19:55:48,423][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:55:48,424][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:55:48,430][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:55:50,887][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:55:50,889][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:55:50,895][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:55:50,897][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:55:50,898][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:55:51,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:51,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:52,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:53,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:54,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:55,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:55,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:56,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:57,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:58,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:59,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:55:59,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:00,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:01,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:02,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:03,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:03,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:04,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:05,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:06,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:07,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:07,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:08,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:09,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:10,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:11,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:12,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:13,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:13,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:14,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:15,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:16,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:17,995][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:56:19,013][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:56:19,015][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:56:20,319][__main__][INFO] - Iteration 759 took 55s (37.64% Gen, 62.35% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 31m 41s. Estimated total time: 15h 17m 30s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 45s. +[2025-08-20 19:56:20,320][__main__][INFO] - Starting iteration 759. +[2025-08-20 19:56:43,459][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:56:43,461][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:56:43,467][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:56:45,921][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:56:45,923][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:56:45,929][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:56:45,931][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:56:45,932][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:56:46,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:47,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:47,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:48,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:49,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:50,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:50,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:51,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:52,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:53,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:54,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:54,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:55,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:56,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:57,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:58,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:58,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:56:59,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:00,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:01,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:02,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:03,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:04,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:04,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:05,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:06,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:07,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:08,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:08,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:09,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:10,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:11,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:12,995][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:57:13,990][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:57:13,992][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:57:15,415][__main__][INFO] - Iteration 760 took 55s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 31m 29s. Estimated total time: 15h 18m 14s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 49s, 500 more iterations: 7h 39m 7s. +[2025-08-20 19:57:15,416][__main__][INFO] - Starting iteration 760. +[2025-08-20 19:57:38,878][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:57:38,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:57:38,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:57:41,335][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:57:41,336][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:57:41,343][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:57:41,345][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:57:41,345][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:57:41,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:42,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:43,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:44,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:44,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:45,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:46,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:47,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:47,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:48,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:49,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:50,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:51,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:51,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:52,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:53,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:54,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:55,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:56,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:57,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:58,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:58,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:57:59,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:00,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:01,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:02,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:02,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:03,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:04,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:05,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:05,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:06,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:08,407][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:58:09,374][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:58:09,375][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:58:10,699][__main__][INFO] - Iteration 761 took 55s (38.03% Gen, 61.97% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 33m 42s. Estimated total time: 15h 21m 21s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 8s, 500 more iterations: 7h 40m 40s. +[2025-08-20 19:58:10,700][__main__][INFO] - Starting iteration 761. +[2025-08-20 19:58:34,289][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:58:34,291][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:58:34,297][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:58:36,740][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:58:36,741][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:58:36,748][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:58:36,750][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:58:36,750][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:58:37,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:37,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:38,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:39,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:40,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:41,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:41,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:42,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:43,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:44,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:44,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:45,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:46,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:47,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:48,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:48,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:49,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:51,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:51,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:52,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:53,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:54,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:55,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:55,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:56,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:57,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:58,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:59,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:58:59,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:00,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:01,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:02,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:03,864][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:59:04,801][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:59:04,802][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 19:59:06,045][__main__][INFO] - Iteration 762 took 55s (38.21% Gen, 61.79% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 33m 49s. Estimated total time: 15h 22m 24s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 14s, 500 more iterations: 7h 41m 12s. +[2025-08-20 19:59:06,046][__main__][INFO] - Starting iteration 762. +[2025-08-20 19:59:29,424][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:59:29,425][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:59:29,431][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:59:31,865][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:59:31,866][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:59:31,872][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 19:59:31,874][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 19:59:31,875][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 19:59:32,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:32,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:33,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:34,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:35,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:36,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:36,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:37,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:38,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:39,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:40,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:40,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:41,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:42,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:43,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:44,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:44,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:46,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:46,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:47,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:48,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:49,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:50,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:50,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:51,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:52,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:53,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:54,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:54,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:55,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:56,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:57,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 19:59:58,863][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 19:59:59,968][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 19:59:59,970][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:00:01,313][__main__][INFO] - Iteration 763 took 55s (37.90% Gen, 62.10% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 31m 35s. Estimated total time: 15h 21m 6s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 33s. +[2025-08-20 20:00:01,314][__main__][INFO] - Starting iteration 763. +[2025-08-20 20:00:25,007][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:00:25,008][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:00:25,014][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:00:27,463][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:00:27,464][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:00:27,471][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:00:27,473][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:00:27,474][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:00:27,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:28,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:29,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:30,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:30,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:31,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:32,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:33,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:34,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:34,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:35,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:36,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:37,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:38,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:38,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:39,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:40,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:41,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:42,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:42,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:43,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:44,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:45,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:46,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:46,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:47,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:48,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:49,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:50,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:51,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:52,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:52,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:00:54,493][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:00:55,480][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:00:55,481][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:00:56,985][__main__][INFO] - Iteration 764 took 55s (38.16% Gen, 61.84% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 37m 24s. Estimated total time: 15h 27m 50s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 47s, 500 more iterations: 7h 43m 55s. +[2025-08-20 20:00:56,987][__main__][INFO] - Starting iteration 764. +[2025-08-20 20:01:20,099][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:01:20,100][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:01:20,106][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:01:22,574][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:01:22,575][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:01:22,582][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:01:22,584][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:01:22,585][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:01:22,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:23,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:24,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:25,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:26,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:26,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:27,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:28,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:29,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:30,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:30,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:31,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:32,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:33,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:34,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:34,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:35,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:36,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:37,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:37,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:38,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:39,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:40,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:41,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:42,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:43,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:44,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:44,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:45,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:46,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:47,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:48,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:01:49,641][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:01:50,579][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:01:50,580][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:01:51,949][__main__][INFO] - Iteration 765 took 54s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 24m 40s. Estimated total time: 15h 16m 1s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 0s. +[2025-08-20 20:01:51,950][__main__][INFO] - Starting iteration 765. +[2025-08-20 20:02:15,395][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:02:15,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:02:15,402][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:02:17,848][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:02:17,850][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:02:17,856][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:02:17,858][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:02:17,859][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:02:18,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:18,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:19,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:20,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:21,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:22,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:22,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:23,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:24,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:25,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:26,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:26,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:27,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:28,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:29,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:30,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:30,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:31,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:32,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:33,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:34,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:34,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:35,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:36,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:37,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:38,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:39,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:40,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:40,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:41,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:42,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:43,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:02:44,816][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:02:45,743][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:02:45,744][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:02:47,105][__main__][INFO] - Iteration 766 took 55s (38.08% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 26m 58s. Estimated total time: 15h 19m 14s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 37s. +[2025-08-20 20:02:47,107][__main__][INFO] - Starting iteration 766. +[2025-08-20 20:03:10,138][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:03:10,139][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:03:10,145][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:03:12,613][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:03:12,615][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:03:12,621][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:03:12,623][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:03:12,624][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:03:12,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:13,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:14,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:15,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:16,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:16,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:17,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:18,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:19,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:20,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:20,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:21,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:22,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:23,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:24,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:24,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:25,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:26,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:27,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:28,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:28,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:29,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:30,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:31,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:32,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:33,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:34,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:34,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:35,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:36,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:37,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:38,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:03:39,683][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:03:40,610][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:03:40,612][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:03:42,063][__main__][INFO] - Iteration 767 took 54s (37.46% Gen, 62.54% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 22m 44s. Estimated total time: 15h 15m 55s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 57s. +[2025-08-20 20:03:42,064][__main__][INFO] - Starting iteration 767. +[2025-08-20 20:04:05,533][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:04:05,534][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:04:05,541][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:04:07,991][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:04:07,993][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:04:07,999][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:04:08,002][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:04:08,002][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:04:08,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:09,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:09,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:10,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:11,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:12,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:13,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:13,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:14,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:15,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:16,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:17,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:17,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:18,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:19,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:20,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:21,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:21,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:22,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:23,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:24,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:25,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:25,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:26,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:27,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:28,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:28,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:30,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:31,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:31,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:32,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:33,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:04:35,140][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:04:36,093][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:04:36,094][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:04:37,425][__main__][INFO] - Iteration 768 took 55s (38.00% Gen, 62.00% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 28m 34s. Estimated total time: 15h 22m 40s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 16s, 500 more iterations: 7h 41m 20s. +[2025-08-20 20:04:37,427][__main__][INFO] - Starting iteration 768. +[2025-08-20 20:05:00,469][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:00,471][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:00,477][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:02,942][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:02,944][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:02,950][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:02,952][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:05:02,953][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:05:03,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:04,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:04,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:05,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:06,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:07,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:07,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:08,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:09,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:10,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:11,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:11,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:12,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:13,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:14,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:15,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:15,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:16,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:17,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:18,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:19,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:20,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:21,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:22,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:22,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:23,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:24,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:25,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:25,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:26,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:27,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:28,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:29,951][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:05:30,881][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:05:30,883][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:05:32,252][__main__][INFO] - Iteration 769 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 18m 43s. Estimated total time: 15h 13m 44s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 52s. +[2025-08-20 20:05:32,254][__main__][INFO] - Starting iteration 769. +[2025-08-20 20:05:55,365][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:55,366][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:55,372][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:57,823][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:57,824][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:57,831][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:05:57,833][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:05:57,833][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:05:58,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:58,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:05:59,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:00,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:01,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:02,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:02,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:03,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:04,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:05,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:06,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:06,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:07,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:08,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:09,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:10,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:10,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:11,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:12,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:13,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:13,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:14,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:15,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:16,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:17,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:17,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:18,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:20,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:20,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:21,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:22,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:23,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:24,827][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:06:25,751][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:06:25,752][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:06:27,065][__main__][INFO] - Iteration 770 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 17m 35s. Estimated total time: 15h 13m 31s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 21s, 500 more iterations: 7h 36m 45s. +[2025-08-20 20:06:27,067][__main__][INFO] - Starting iteration 770. +[2025-08-20 20:06:50,149][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:06:50,150][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:06:50,156][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:06:52,575][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:06:52,577][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:06:52,583][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:06:52,585][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:06:52,586][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:06:52,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:53,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:54,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:55,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:56,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:56,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:57,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:58,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:06:59,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:00,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:00,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:01,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:02,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:03,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:04,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:04,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:05,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:06,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:07,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:07,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:08,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:10,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:10,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:11,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:12,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:13,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:14,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:14,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:15,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:16,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:17,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:18,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:19,746][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:07:20,705][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:07:20,706][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:07:21,997][__main__][INFO] - Iteration 771 took 54s (37.60% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 18m 39s. Estimated total time: 15h 15m 30s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 45s. +[2025-08-20 20:07:21,999][__main__][INFO] - Starting iteration 771. +[2025-08-20 20:07:45,403][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:07:45,404][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:07:45,410][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:07:47,877][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:07:47,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:07:47,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:07:47,888][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:07:47,889][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:07:48,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:48,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:49,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:50,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:51,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:52,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:52,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:53,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:54,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:55,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:56,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:56,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:57,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:58,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:07:59,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:00,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:00,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:01,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:02,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:03,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:04,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:04,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:05,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:06,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:07,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:08,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:09,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:10,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:10,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:11,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:12,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:13,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:14,881][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:08:15,941][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:08:15,943][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:08:17,475][__main__][INFO] - Iteration 772 took 55s (37.78% Gen, 62.22% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 26m 49s. Estimated total time: 15h 24m 35s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 27s, 500 more iterations: 7h 42m 17s. +[2025-08-20 20:08:17,476][__main__][INFO] - Starting iteration 772. +[2025-08-20 20:08:40,502][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:08:40,504][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:08:40,510][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:08:42,979][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:08:42,980][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:08:42,987][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:08:42,989][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:08:42,990][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:08:43,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:44,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:44,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:45,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:46,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:47,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:48,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:48,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:49,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:50,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:51,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:52,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:52,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:53,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:54,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:55,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:55,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:57,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:58,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:58,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:08:59,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:00,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:01,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:02,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:02,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:03,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:04,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:05,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:06,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:06,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:07,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:08,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:10,041][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:09:10,968][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:09:10,969][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:09:12,297][__main__][INFO] - Iteration 773 took 54s (37.56% Gen, 62.44% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 14m 59s. Estimated total time: 15h 13m 40s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 50s. +[2025-08-20 20:09:12,298][__main__][INFO] - Starting iteration 773. +[2025-08-20 20:09:35,331][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:09:35,332][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:09:35,338][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:09:37,783][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:09:37,784][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:09:37,791][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:09:37,793][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:09:37,793][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:09:38,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:38,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:39,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:40,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:41,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:42,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:42,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:43,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:44,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:45,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:46,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:46,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:47,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:48,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:49,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:50,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:50,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:51,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:52,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:53,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:54,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:55,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:56,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:56,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:57,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:58,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:09:59,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:00,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:00,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:01,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:02,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:03,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:04,873][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:10:05,835][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:10:05,836][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:10:08,195][__main__][INFO] - Iteration 774 took 55s (36.84% Gen, 63.16% Train). Generation: 20s, Training: 35s. Estimated remaining time: 3h 31m 58s. Estimated total time: 15h 31m 36s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 9s, 500 more iterations: 7h 45m 48s. +[2025-08-20 20:10:08,196][__main__][INFO] - Starting iteration 774. +[2025-08-20 20:10:31,689][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:10:31,691][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:10:31,697][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:10:34,145][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:10:34,146][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:10:34,152][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:10:34,155][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:10:34,156][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:10:34,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:35,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:36,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:36,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:37,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:38,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:39,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:40,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:40,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:41,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:42,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:43,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:43,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:44,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:45,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:46,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:47,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:47,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:48,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:49,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:50,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:51,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:52,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:53,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:54,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:54,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:55,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:56,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:57,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:58,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:58,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:10:59,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:01,183][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:11:02,088][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:11:02,090][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:11:03,463][__main__][INFO] - Iteration 775 took 55s (38.08% Gen, 61.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 20m 33s. Estimated total time: 15h 21m 6s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 33s. +[2025-08-20 20:11:03,465][__main__][INFO] - Starting iteration 775. +[2025-08-20 20:11:26,718][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:11:26,719][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:11:26,726][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:11:29,192][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:11:29,193][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:11:29,200][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:11:29,202][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:11:29,202][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:11:29,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:30,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:31,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:31,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:32,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:33,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:34,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:35,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:35,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:36,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:37,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:38,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:39,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:39,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:40,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:41,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:42,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:43,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:44,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:45,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:45,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:46,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:47,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:48,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:49,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:49,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:50,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:51,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:52,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:53,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:53,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:54,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:11:56,272][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:11:57,183][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:11:57,184][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:11:58,445][__main__][INFO] - Iteration 776 took 54s (37.82% Gen, 62.18% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 14m 52s. Estimated total time: 15h 16m 19s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 9s. +[2025-08-20 20:11:58,446][__main__][INFO] - Starting iteration 776. +[2025-08-20 20:12:22,079][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:12:22,081][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:12:22,087][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:12:24,540][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:12:24,541][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:12:24,548][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:12:24,550][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:12:24,550][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:12:24,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:25,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:26,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:27,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:28,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:28,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:29,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:30,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:31,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:32,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:32,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:33,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:34,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:35,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:35,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:36,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:37,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:38,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:39,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:39,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:40,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:41,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:42,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:43,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:44,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:45,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:45,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:46,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:47,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:48,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:49,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:49,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:12:51,564][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:12:52,533][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:12:52,534][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:12:54,290][__main__][INFO] - Iteration 777 took 55s (37.93% Gen, 62.07% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 28m 20s. Estimated total time: 15h 30m 43s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 4s, 500 more iterations: 7h 45m 21s. +[2025-08-20 20:12:54,292][__main__][INFO] - Starting iteration 777. +[2025-08-20 20:13:18,601][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:13:18,603][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:13:18,609][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:13:21,074][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:13:21,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:13:21,082][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:13:21,084][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:13:21,085][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:13:21,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:22,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:22,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:23,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:24,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:25,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:26,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:26,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:27,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:28,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:29,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:30,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:30,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:31,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:32,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:33,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:34,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:34,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:35,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:36,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:37,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:38,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:38,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:39,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:40,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:41,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:42,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:43,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:44,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:44,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:45,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:46,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:13:48,073][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:13:48,987][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:13:48,988][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:13:50,321][__main__][INFO] - Iteration 778 took 56s (38.99% Gen, 61.01% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 30m 29s. Estimated total time: 15h 33m 48s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 22s, 500 more iterations: 7h 46m 54s. +[2025-08-20 20:13:50,322][__main__][INFO] - Starting iteration 778. +[2025-08-20 20:14:13,547][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:14:13,549][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:14:13,556][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:14:16,054][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:14:16,056][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:14:16,062][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:14:16,065][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:14:16,065][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:14:16,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:17,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:17,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:18,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:19,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:20,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:21,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:21,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:22,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:23,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:24,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:25,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:25,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:26,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:27,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:28,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:29,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:29,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:30,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:31,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:32,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:33,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:34,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:35,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:36,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:36,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:37,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:38,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:39,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:39,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:40,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:41,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:14:43,156][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:14:44,143][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:14:44,144][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:14:45,373][__main__][INFO] - Iteration 779 took 55s (37.64% Gen, 62.36% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 13m 15s. Estimated total time: 15h 17m 30s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 45s. +[2025-08-20 20:14:45,375][__main__][INFO] - Starting iteration 779. +[2025-08-20 20:15:08,751][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:15:08,753][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:15:08,759][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:15:11,194][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:15:11,195][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:15:11,202][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:15:11,204][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:15:11,205][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:15:11,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:12,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:13,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:13,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:14,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:15,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:16,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:17,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:17,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:18,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:19,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:20,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:21,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:21,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:22,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:23,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:24,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:25,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:25,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:26,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:27,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:28,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:29,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:30,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:31,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:31,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:32,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:33,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:34,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:34,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:35,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:36,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:15:38,166][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:15:39,134][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:15:39,135][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:15:40,636][__main__][INFO] - Iteration 780 took 55s (37.90% Gen, 62.10% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 15m 51s. Estimated total time: 15h 21m 1s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 6s, 500 more iterations: 7h 40m 30s. +[2025-08-20 20:15:40,638][__main__][INFO] - Starting iteration 780. +[2025-08-20 20:16:04,162][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:04,163][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:04,170][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:06,643][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:06,644][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:06,651][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:06,654][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:16:06,654][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:16:06,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:07,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:08,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:09,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:10,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:10,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:11,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:12,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:13,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:14,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:14,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:15,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:16,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:17,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:18,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:18,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:19,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:20,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:21,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:22,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:22,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:23,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:24,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:25,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:26,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:26,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:27,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:28,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:29,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:30,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:31,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:32,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:16:33,654][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:16:34,628][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:16:34,630][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:16:36,035][__main__][INFO] - Iteration 781 took 55s (38.04% Gen, 61.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 17m 11s. Estimated total time: 15h 23m 16s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 38s. +[2025-08-20 20:16:36,037][__main__][INFO] - Starting iteration 781. +[2025-08-20 20:16:59,612][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:59,614][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:16:59,621][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:02,080][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:02,081][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:02,087][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:02,090][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:17:02,090][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:17:02,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:03,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:03,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:04,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:05,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:06,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:07,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:07,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:08,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:09,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:10,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:11,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:11,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:12,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:13,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:14,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:15,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:15,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:16,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:17,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:18,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:19,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:20,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:21,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:21,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:22,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:23,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:24,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:25,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:25,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:26,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:27,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:29,168][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:17:30,578][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:17:30,580][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:17:31,846][__main__][INFO] - Iteration 782 took 55s (37.86% Gen, 62.14% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 23m 8s. Estimated total time: 15h 30m 9s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 0s, 500 more iterations: 7h 45m 4s. +[2025-08-20 20:17:31,847][__main__][INFO] - Starting iteration 782. +[2025-08-20 20:17:55,400][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:55,402][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:55,408][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:57,858][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:57,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:57,866][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:17:57,868][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:17:57,869][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:17:58,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:58,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:17:59,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:00,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:01,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:02,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:02,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:03,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:04,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:05,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:06,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:06,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:07,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:08,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:09,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:10,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:10,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:11,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:12,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:13,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:14,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:15,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:16,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:16,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:17,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:18,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:19,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:20,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:20,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:21,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:22,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:23,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:24,882][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:18:25,788][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:18:25,790][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:18:27,129][__main__][INFO] - Iteration 783 took 55s (38.16% Gen, 61.84% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 13m 25s. Estimated total time: 15h 21m 21s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 8s, 500 more iterations: 7h 40m 40s. +[2025-08-20 20:18:27,131][__main__][INFO] - Starting iteration 783. +[2025-08-20 20:18:50,228][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:18:50,230][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:18:50,236][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:18:52,696][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:18:52,698][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:18:52,704][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:18:52,706][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:18:52,707][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:18:53,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:53,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:54,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:55,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:56,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:56,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:57,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:58,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:18:59,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:00,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:00,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:01,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:02,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:03,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:04,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:04,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:05,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:06,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:07,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:08,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:09,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:10,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:10,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:11,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:12,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:13,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:14,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:14,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:15,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:16,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:17,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:18,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:19,652][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:19:20,575][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:19:20,576][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:19:22,019][__main__][INFO] - Iteration 784 took 54s (37.58% Gen, 62.42% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 5m 56s. Estimated total time: 15h 14m 47s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 23s. +[2025-08-20 20:19:22,021][__main__][INFO] - Starting iteration 784. +[2025-08-20 20:19:45,157][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:19:45,158][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:19:45,164][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:19:47,635][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:19:47,636][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:19:47,643][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:19:47,645][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:19:47,646][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:19:47,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:48,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:49,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:50,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:51,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:51,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:52,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:53,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:54,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:55,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:55,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:56,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:57,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:58,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:59,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:19:59,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:00,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:01,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:02,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:03,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:03,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:04,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:05,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:06,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:07,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:07,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:08,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:09,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:10,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:11,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:12,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:13,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:14,778][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:20:15,699][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:20:15,700][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:20:16,957][__main__][INFO] - Iteration 785 took 54s (37.63% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 5m 49s. Estimated total time: 15h 15m 35s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 47s. +[2025-08-20 20:20:16,958][__main__][INFO] - Starting iteration 785. +[2025-08-20 20:20:40,544][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:20:40,545][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:20:40,552][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:20:42,997][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:20:42,998][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:20:43,005][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:20:43,007][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:20:43,007][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:20:43,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:44,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:44,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:45,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:46,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:47,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:48,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:48,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:49,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:50,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:51,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:52,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:52,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:53,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:54,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:55,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:56,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:56,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:57,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:58,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:59,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:20:59,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:01,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:01,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:02,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:03,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:04,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:05,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:05,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:06,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:07,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:08,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:09,950][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:21:11,456][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:21:11,458][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:21:12,839][__main__][INFO] - Iteration 786 took 55s (37.84% Gen, 62.16% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 20m 38s. Estimated total time: 15h 31m 20s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 8s, 500 more iterations: 7h 45m 40s. +[2025-08-20 20:21:12,841][__main__][INFO] - Starting iteration 786. +[2025-08-20 20:21:36,446][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:21:36,448][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:21:36,454][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:21:38,912][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:21:38,913][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:21:38,919][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:21:38,922][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:21:38,922][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:21:39,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:40,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:40,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:41,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:42,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:43,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:43,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:44,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:45,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:46,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:47,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:47,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:48,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:49,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:50,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:51,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:51,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:52,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:53,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:54,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:55,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:56,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:57,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:57,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:58,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:21:59,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:00,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:01,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:01,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:02,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:03,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:04,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:05,901][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:22:06,872][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:22:06,873][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:22:08,336][__main__][INFO] - Iteration 787 took 55s (38.09% Gen, 61.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 13m 17s. Estimated total time: 15h 24m 54s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 29s, 500 more iterations: 7h 42m 27s. +[2025-08-20 20:22:08,337][__main__][INFO] - Starting iteration 787. +[2025-08-20 20:22:31,968][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:22:31,969][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:22:31,975][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:22:34,475][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:22:34,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:22:34,483][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:22:34,485][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:22:34,485][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:22:34,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:35,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:36,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:37,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:37,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:38,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:39,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:40,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:41,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:41,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:42,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:43,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:44,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:45,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:45,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:46,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:47,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:48,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:49,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:50,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:51,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:51,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:52,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:53,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:54,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:55,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:55,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:56,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:57,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:58,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:59,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:22:59,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:01,527][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:23:02,461][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:23:02,463][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:23:03,739][__main__][INFO] - Iteration 788 took 55s (38.14% Gen, 61.86% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 10m 48s. Estimated total time: 15h 23m 20s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 20s, 500 more iterations: 7h 41m 40s. +[2025-08-20 20:23:03,740][__main__][INFO] - Starting iteration 788. +[2025-08-20 20:23:27,833][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:23:27,834][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:23:27,841][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:23:30,299][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:23:30,300][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:23:30,307][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:23:30,309][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:23:30,310][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:23:30,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:31,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:32,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:32,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:33,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:34,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:35,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:36,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:36,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:37,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:38,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:39,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:40,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:40,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:41,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:42,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:43,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:44,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:45,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:46,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:46,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:47,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:48,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:49,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:50,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:50,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:51,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:52,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:53,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:54,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:54,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:55,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:23:57,226][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:23:58,159][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:23:58,161][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:23:59,481][__main__][INFO] - Iteration 789 took 55s (38.82% Gen, 61.18% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 15m 31s. Estimated total time: 15h 29m 0s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 54s, 500 more iterations: 7h 44m 30s. +[2025-08-20 20:23:59,495][__main__][INFO] - Starting iteration 789. +[2025-08-20 20:24:23,033][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:24:23,038][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:24:23,048][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:24:25,501][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:24:25,502][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:24:25,508][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:24:25,511][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:24:25,511][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:24:25,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:26,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:27,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:28,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:29,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:29,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:30,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:31,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:32,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:33,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:33,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:34,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:35,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:36,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:36,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:37,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:38,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:39,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:40,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:40,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:41,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:42,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:43,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:44,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:45,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:46,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:46,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:47,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:48,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:49,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:50,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:50,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:24:52,565][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:24:53,492][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:24:53,493][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:24:54,844][__main__][INFO] - Iteration 790 took 55s (38.08% Gen, 61.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 7m 49s. Estimated total time: 15h 22m 13s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 13s, 500 more iterations: 7h 41m 6s. +[2025-08-20 20:24:54,845][__main__][INFO] - Starting iteration 790. +[2025-08-20 20:25:18,547][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:25:18,548][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:25:18,554][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:25:21,025][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:25:21,027][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:25:21,033][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:25:21,036][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:25:21,036][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:25:21,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:22,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:22,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:23,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:24,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:25,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:26,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:26,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:27,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:28,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:29,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:30,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:30,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:31,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:32,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:33,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:34,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:34,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:35,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:36,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:37,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:38,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:38,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:40,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:40,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:41,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:42,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:43,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:44,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:44,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:45,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:46,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:25:48,136][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:25:49,031][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:25:49,032][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:25:50,244][__main__][INFO] - Iteration 791 took 55s (38.35% Gen, 61.64% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 7m 59s. Estimated total time: 15h 23m 18s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 19s, 500 more iterations: 7h 41m 39s. +[2025-08-20 20:25:50,245][__main__][INFO] - Starting iteration 791. +[2025-08-20 20:26:13,602][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:26:13,604][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:26:13,610][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:26:16,070][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:26:16,072][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:26:16,078][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:26:16,080][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:26:16,081][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:26:16,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:17,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:17,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:18,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:19,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:20,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:21,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:21,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:22,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:23,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:24,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:25,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:25,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:26,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:27,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:28,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:29,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:29,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:30,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:31,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:32,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:33,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:34,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:35,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:35,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:36,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:37,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:38,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:39,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:39,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:40,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:41,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:26:43,092][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:26:44,026][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:26:44,028][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:26:45,367][__main__][INFO] - Iteration 792 took 55s (37.94% Gen, 62.06% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 2m 27s. Estimated total time: 15h 18m 41s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 20s. +[2025-08-20 20:26:45,369][__main__][INFO] - Starting iteration 792. +[2025-08-20 20:27:09,278][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:27:09,279][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:27:09,285][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:27:11,743][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:27:11,744][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:27:11,751][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:27:11,753][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:27:11,753][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:27:12,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:12,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:13,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:14,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:15,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:16,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:16,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:17,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:18,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:19,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:19,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:20,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:21,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:22,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:23,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:23,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:24,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:25,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:26,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:27,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:28,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:29,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:30,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:30,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:31,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:32,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:33,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:33,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:34,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:35,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:36,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:37,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:27:38,760][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:27:39,667][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:27:39,669][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:27:41,093][__main__][INFO] - Iteration 793 took 55s (38.49% Gen, 61.50% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 11m 34s. Estimated total time: 15h 28m 44s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 52s, 500 more iterations: 7h 44m 22s. +[2025-08-20 20:27:41,095][__main__][INFO] - Starting iteration 793. +[2025-08-20 20:28:04,856][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:28:04,857][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:28:04,864][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:28:07,329][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:28:07,331][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:28:07,337][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:28:07,339][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:28:07,340][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:28:07,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:08,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:09,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:10,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:10,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:11,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:12,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:13,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:13,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:14,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:15,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:16,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:17,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:17,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:18,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:19,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:20,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:21,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:22,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:23,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:24,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:24,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:25,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:26,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:27,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:27,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:28,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:29,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:30,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:31,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:31,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:32,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:28:34,389][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:28:35,325][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:28:35,326][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:28:36,563][__main__][INFO] - Iteration 794 took 55s (38.41% Gen, 61.59% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 6m 22s. Estimated total time: 15h 24m 28s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 26s, 500 more iterations: 7h 42m 14s. +[2025-08-20 20:28:36,565][__main__][INFO] - Starting iteration 794. +[2025-08-20 20:29:00,208][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:00,210][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:00,216][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:02,656][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:02,657][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:02,664][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:02,666][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:29:02,667][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:29:02,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:03,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:04,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:05,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:06,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:06,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:07,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:08,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:09,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:10,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:11,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:11,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:12,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:13,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:14,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:14,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:15,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:16,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:17,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:18,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:18,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:19,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:20,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:21,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:22,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:23,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:24,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:24,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:25,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:26,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:27,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:28,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:29,731][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:29:30,700][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:29:30,701][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:29:32,003][__main__][INFO] - Iteration 795 took 55s (38.24% Gen, 61.76% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 4m 56s. Estimated total time: 15h 23m 57s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 23s, 500 more iterations: 7h 41m 58s. +[2025-08-20 20:29:32,006][__main__][INFO] - Starting iteration 795. +[2025-08-20 20:29:55,404][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:55,405][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:55,412][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:57,878][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:57,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:57,886][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:29:57,888][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:29:57,889][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:29:58,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:58,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:29:59,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:00,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:01,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:02,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:02,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:03,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:04,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:05,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:06,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:06,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:07,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:08,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:09,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:10,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:10,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:11,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:12,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:13,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:14,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:15,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:16,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:16,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:17,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:18,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:19,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:20,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:20,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:21,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:22,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:23,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:24,880][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:30:25,837][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:30:25,839][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:30:27,115][__main__][INFO] - Iteration 796 took 55s (37.99% Gen, 62.01% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2h 58m 30s. Estimated total time: 15h 18m 26s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 50s, 500 more iterations: 7h 39m 13s. +[2025-08-20 20:30:27,117][__main__][INFO] - Starting iteration 796. +[2025-08-20 20:30:50,575][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:30:50,577][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:30:50,583][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:30:53,055][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:30:53,057][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:30:53,064][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:30:53,066][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:30:53,066][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:30:53,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:54,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:54,948][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:55,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:56,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:57,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:58,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:58,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:30:59,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:00,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:01,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:02,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:02,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:03,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:04,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:05,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:06,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:06,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:07,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:08,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:09,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:10,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:10,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:11,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:12,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:13,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:14,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:15,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:16,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:16,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:17,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:18,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:20,093][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:31:21,052][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:31:21,054][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:31:22,343][__main__][INFO] - Iteration 797 took 55s (38.01% Gen, 61.98% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2h 59m 35s. Estimated total time: 15h 20m 26s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 2s, 500 more iterations: 7h 40m 13s. +[2025-08-20 20:31:22,345][__main__][INFO] - Starting iteration 797. +[2025-08-20 20:31:45,873][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:31:45,874][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:31:45,880][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:31:48,323][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:31:48,324][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:31:48,331][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:31:48,333][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:31:48,334][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:31:48,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:49,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:50,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:51,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:51,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:52,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:53,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:54,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:54,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:55,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:56,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:57,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:58,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:58,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:31:59,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:00,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:01,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:02,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:03,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:04,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:05,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:05,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:06,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:07,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:08,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:08,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:09,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:10,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:11,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:12,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:12,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:13,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:15,329][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:32:16,300][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:32:16,301][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:32:17,619][__main__][INFO] - Iteration 798 took 55s (38.12% Gen, 61.88% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 59m 27s. Estimated total time: 15h 21m 13s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 7s, 500 more iterations: 7h 40m 36s. +[2025-08-20 20:32:17,620][__main__][INFO] - Starting iteration 798. +[2025-08-20 20:32:41,252][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:32:41,253][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:32:41,259][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:32:43,706][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:32:43,707][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:32:43,714][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:32:43,716][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:32:43,716][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:32:44,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:44,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:45,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:46,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:47,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:47,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:48,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:49,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:50,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:51,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:51,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:52,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:53,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:54,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:55,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:55,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:56,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:57,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:58,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:32:59,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:00,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:01,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:01,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:02,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:03,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:04,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:05,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:05,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:06,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:07,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:08,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:09,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:10,676][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:33:11,555][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:33:11,557][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:33:12,828][__main__][INFO] - Iteration 799 took 55s (38.38% Gen, 61.61% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 57m 25s. Estimated total time: 15h 20m 7s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 0s, 500 more iterations: 7h 40m 3s. +[2025-08-20 20:33:12,830][__main__][INFO] - Starting iteration 799. +[2025-08-20 20:33:36,361][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:33:36,362][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:33:36,368][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:33:38,797][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:33:38,798][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:33:38,804][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:33:38,807][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:33:38,807][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:33:39,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:39,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:40,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:41,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:42,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:43,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:43,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:45,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:45,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:46,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:47,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:48,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:49,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:49,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:50,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:51,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:52,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:53,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:53,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:54,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:55,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:56,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:56,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:57,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:58,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:33:59,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:00,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:01,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:02,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:03,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:03,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:04,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:06,264][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:34:07,227][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:34:07,228][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:34:08,563][__main__][INFO] - Iteration 800 took 55s (37.87% Gen, 62.13% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 5m 14s. Estimated total time: 15h 28m 52s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 53s, 500 more iterations: 7h 44m 26s. +[2025-08-20 20:34:08,564][__main__][INFO] - Starting iteration 800. +[2025-08-20 20:34:31,849][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:34:31,851][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:34:31,857][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:34:34,315][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:34:34,317][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:34:34,323][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:34:34,326][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:34:34,326][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:34:34,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:35,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:36,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:36,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:37,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:38,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:39,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:40,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:40,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:41,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:42,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:43,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:44,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:44,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:45,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:46,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:47,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:48,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:48,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:49,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:50,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:51,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:52,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:52,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:53,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:54,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:55,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:56,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:57,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:58,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:58,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:34:59,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:01,332][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:35:02,234][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:35:02,236][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:35:06,218][__main__][INFO] - Iteration 801 took 57s (36.12% Gen, 59.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3h 36m 18s. Estimated total time: 16h 0m 53s. Time estimates for 10 more iterations: 9m 36s, 100 more iterations: 1h 36m 5s, 500 more iterations: 8h 0m 26s. +[2025-08-20 20:35:06,220][__main__][INFO] - Starting iteration 801. +[2025-08-20 20:35:30,032][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:35:30,033][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:35:30,040][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:35:32,496][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:35:32,497][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:35:32,503][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:35:32,505][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:35:32,506][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:35:32,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:33,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:34,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:35,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:35,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:36,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:37,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:38,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:39,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:39,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:40,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:41,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:42,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:43,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:43,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:44,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:45,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:46,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:47,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:47,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:48,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:49,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:50,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:51,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:51,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:52,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:53,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:54,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:55,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:56,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:57,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:57,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:35:59,489][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:36:00,409][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:36:00,411][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:36:01,710][__main__][INFO] - Iteration 802 took 55s (38.48% Gen, 61.52% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 59m 18s. Estimated total time: 15h 24m 49s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 28s, 500 more iterations: 7h 42m 24s. +[2025-08-20 20:36:01,711][__main__][INFO] - Starting iteration 802. +[2025-08-20 20:36:25,780][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:36:25,781][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:36:25,787][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:36:28,265][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:36:28,266][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:36:28,272][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:36:28,274][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:36:28,275][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:36:28,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:29,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:30,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:30,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:31,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:32,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:33,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:34,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:34,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:35,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:36,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:37,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:38,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:38,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:39,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:40,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:41,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:42,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:42,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:43,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:44,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:45,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:46,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:47,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:48,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:48,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:49,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:50,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:51,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:52,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:52,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:53,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:36:55,357][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:36:56,285][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:36:56,286][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:36:57,689][__main__][INFO] - Iteration 803 took 55s (38.56% Gen, 61.44% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 6m 31s. Estimated total time: 15h 32m 58s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 17s, 500 more iterations: 7h 46m 29s. +[2025-08-20 20:36:57,691][__main__][INFO] - Starting iteration 803. +[2025-08-20 20:37:21,055][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:37:21,057][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:37:21,063][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:37:23,518][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:37:23,519][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:37:23,526][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:37:23,528][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:37:23,528][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:37:23,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:24,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:25,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:26,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:27,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:27,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:28,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:29,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:30,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:30,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:31,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:32,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:33,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:34,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:34,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:35,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:36,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:37,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:38,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:39,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:40,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:40,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:41,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:42,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:43,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:44,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:44,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:45,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:46,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:47,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:48,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:48,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:37:50,484][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:37:51,420][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:37:51,421][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:37:52,816][__main__][INFO] - Iteration 804 took 55s (37.93% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2h 51m 22s. Estimated total time: 15h 18m 44s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 52s, 500 more iterations: 7h 39m 22s. +[2025-08-20 20:37:52,817][__main__][INFO] - Starting iteration 804. +[2025-08-20 20:38:16,830][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:38:16,831][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:38:16,837][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:38:19,283][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:38:19,285][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:38:19,291][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:38:19,293][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:38:19,294][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:38:19,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:20,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:21,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:21,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:22,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:23,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:24,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:25,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:25,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:26,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:27,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:28,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:29,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:29,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:30,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:31,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:32,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:33,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:33,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:34,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:35,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:36,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:37,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:38,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:39,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:39,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:40,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:41,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:42,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:43,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:43,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:44,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:38:46,277][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:38:47,177][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:38:47,179][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:38:48,584][__main__][INFO] - Iteration 805 took 55s (38.69% Gen, 61.31% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 1m 8s. Estimated total time: 15h 29m 26s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 56s, 500 more iterations: 7h 44m 43s. +[2025-08-20 20:38:48,585][__main__][INFO] - Starting iteration 805. +[2025-08-20 20:39:11,760][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:39:11,761][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:39:11,767][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:39:14,226][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:39:14,227][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:39:14,233][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:39:14,235][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:39:14,236][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:39:14,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:15,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:16,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:16,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:17,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:18,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:19,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:20,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:20,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:21,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:22,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:23,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:24,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:24,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:25,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:26,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:27,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:28,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:28,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:29,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:30,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:31,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:32,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:33,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:34,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:34,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:35,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:36,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:37,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:38,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:38,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:39,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:39:41,364][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:39:42,322][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:39:42,324][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:39:43,570][__main__][INFO] - Iteration 806 took 54s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2h 47m 12s. Estimated total time: 15h 16m 24s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 38s, 500 more iterations: 7h 38m 12s. +[2025-08-20 20:39:43,572][__main__][INFO] - Starting iteration 806. +[2025-08-20 20:40:07,267][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:40:07,269][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:40:07,275][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:40:09,725][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:40:09,726][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:40:09,733][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:40:09,735][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:40:09,735][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:40:10,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:10,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:11,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:12,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:13,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:14,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:14,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:15,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:16,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:17,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:17,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:18,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:19,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:20,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:21,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:21,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:22,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:23,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:24,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:25,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:25,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:26,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:27,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:28,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:29,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:30,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:31,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:31,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:32,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:33,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:34,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:35,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:40:36,941][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:40:37,904][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:40:37,905][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:40:39,247][__main__][INFO] - Iteration 807 took 55s (38.15% Gen, 61.85% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 57m 46s. Estimated total time: 15h 27m 54s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 47s, 500 more iterations: 7h 43m 57s. +[2025-08-20 20:40:39,248][__main__][INFO] - Starting iteration 807. +[2025-08-20 20:41:02,882][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:02,883][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:02,889][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:05,347][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:05,348][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:05,355][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:05,357][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:41:05,358][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:41:05,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:06,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:07,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:08,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:08,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:09,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:10,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:11,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:12,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:12,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:13,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:14,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:15,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:15,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:16,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:17,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:18,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:19,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:19,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:21,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:22,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:22,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:23,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:24,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:25,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:25,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:26,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:27,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:28,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:29,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:29,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:30,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:41:32,382][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:41:33,345][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:41:33,347][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:41:34,634][__main__][INFO] - Iteration 808 took 55s (38.26% Gen, 61.74% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 52m 1s. Estimated total time: 15h 23m 5s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 18s, 500 more iterations: 7h 41m 32s. +[2025-08-20 20:41:34,635][__main__][INFO] - Starting iteration 808. +[2025-08-20 20:41:58,226][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:58,228][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:41:58,234][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:00,709][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:00,711][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:00,717][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:00,719][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:42:00,720][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:42:01,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:01,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:02,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:03,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:04,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:04,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:05,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:06,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:07,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:08,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:08,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:09,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:10,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:11,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:12,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:12,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:13,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:14,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:15,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:16,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:17,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:18,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:19,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:19,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:20,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:21,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:22,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:23,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:23,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:24,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:25,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:26,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:27,889][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:42:28,817][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:42:28,818][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:42:30,083][__main__][INFO] - Iteration 809 took 55s (38.13% Gen, 61.87% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 52m 8s. Estimated total time: 15h 24m 7s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 24s, 500 more iterations: 7h 42m 3s. +[2025-08-20 20:42:30,084][__main__][INFO] - Starting iteration 809. +[2025-08-20 20:42:53,445][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:53,447][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:53,453][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:55,905][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:55,907][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:55,914][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:42:55,915][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:42:55,916][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:42:56,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:57,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:57,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:58,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:42:59,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:00,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:00,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:01,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:02,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:03,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:04,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:04,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:05,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:06,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:07,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:08,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:08,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:10,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:10,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:11,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:12,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:13,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:14,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:14,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:15,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:16,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:17,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:18,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:18,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:19,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:20,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:21,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:22,879][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:43:23,777][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:43:23,778][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:43:25,088][__main__][INFO] - Iteration 810 took 55s (38.02% Gen, 61.97% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2h 43m 49s. Estimated total time: 15h 16m 43s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 21s. +[2025-08-20 20:43:25,090][__main__][INFO] - Starting iteration 810. +[2025-08-20 20:43:49,047][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:43:49,048][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:43:49,055][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:43:51,506][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:43:51,508][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:43:51,514][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:43:51,516][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:43:51,517][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:43:51,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:52,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:53,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:54,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:54,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:55,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:56,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:57,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:58,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:58,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:43:59,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:00,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:01,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:02,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:02,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:03,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:04,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:05,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:06,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:07,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:08,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:09,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:09,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:10,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:11,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:12,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:13,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:13,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:14,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:15,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:16,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:17,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:18,651][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:44:19,605][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:44:19,606][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:44:20,945][__main__][INFO] - Iteration 811 took 55s (38.51% Gen, 61.49% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 57m 5s. Estimated total time: 15h 30m 55s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 5s, 500 more iterations: 7h 45m 27s. +[2025-08-20 20:44:20,947][__main__][INFO] - Starting iteration 811. +[2025-08-20 20:44:44,287][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:44:44,288][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:44:44,295][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:44:46,763][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:44:46,764][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:44:46,771][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:44:46,773][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:44:46,773][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:44:47,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:47,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:48,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:49,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:50,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:51,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:51,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:52,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:53,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:54,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:55,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:55,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:56,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:57,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:58,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:58,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:44:59,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:00,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:01,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:02,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:02,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:03,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:04,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:05,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:06,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:07,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:08,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:09,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:09,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:10,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:11,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:12,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:13,897][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:45:14,836][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:45:14,837][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:45:16,090][__main__][INFO] - Iteration 812 took 55s (37.84% Gen, 62.16% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2h 44m 18s. Estimated total time: 15h 19m 3s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 54s, 500 more iterations: 7h 39m 31s. +[2025-08-20 20:45:16,092][__main__][INFO] - Starting iteration 812. +[2025-08-20 20:45:39,933][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:45:39,934][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:45:39,941][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:45:42,389][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:45:42,391][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:45:42,397][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:45:42,399][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:45:42,400][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:45:42,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:43,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:44,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:45,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:45,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:46,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:47,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:48,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:49,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:49,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:50,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:51,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:52,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:53,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:53,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:54,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:55,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:56,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:56,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:57,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:58,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:45:59,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:00,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:00,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:02,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:02,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:03,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:04,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:05,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:06,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:06,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:07,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:09,366][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:46:10,306][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:46:10,308][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:46:11,625][__main__][INFO] - Iteration 813 took 55s (38.53% Gen, 61.47% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 49m 52s. Estimated total time: 15h 25m 33s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 33s, 500 more iterations: 7h 42m 46s. +[2025-08-20 20:46:11,627][__main__][INFO] - Starting iteration 813. +[2025-08-20 20:46:35,509][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:46:35,511][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:46:35,517][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:46:38,003][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:46:38,004][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:46:38,010][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:46:38,013][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:46:38,013][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:46:38,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:39,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:39,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:40,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:41,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:42,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:43,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:43,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:44,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:45,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:46,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:47,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:47,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:48,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:49,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:50,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:51,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:51,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:52,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:53,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:54,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:55,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:55,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:56,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:57,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:58,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:58,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:46:59,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:01,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:01,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:02,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:03,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:05,058][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:47:05,988][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:47:05,989][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:47:07,578][__main__][INFO] - Iteration 814 took 55s (38.26% Gen, 61.74% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 55m 54s. Estimated total time: 15h 32m 31s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 15s, 500 more iterations: 7h 46m 15s. +[2025-08-20 20:47:07,580][__main__][INFO] - Starting iteration 814. +[2025-08-20 20:47:31,563][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:47:31,564][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:47:31,570][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:47:34,027][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:47:34,028][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:47:34,034][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:47:34,037][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:47:34,037][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:47:34,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:35,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:35,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:36,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:37,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:38,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:39,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:39,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:40,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:41,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:42,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:43,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:43,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:44,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:45,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:46,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:47,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:47,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:48,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:49,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:50,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:51,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:51,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:53,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:53,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:54,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:55,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:56,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:57,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:57,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:58,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:47:59,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:01,133][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:48:02,056][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:48:02,057][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:48:03,274][__main__][INFO] - Iteration 815 took 55s (38.67% Gen, 61.33% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 50m 41s. Estimated total time: 15h 28m 13s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 49s, 500 more iterations: 7h 44m 6s. +[2025-08-20 20:48:03,275][__main__][INFO] - Starting iteration 815. +[2025-08-20 20:48:27,476][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:48:27,477][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:48:27,484][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:48:29,930][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:48:29,931][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:48:29,938][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:48:29,940][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:48:29,940][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:48:30,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:31,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:31,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:32,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:33,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:34,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:34,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:35,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:36,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:37,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:38,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:38,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:39,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:40,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:41,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:42,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:42,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:43,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:44,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:45,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:46,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:47,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:48,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:48,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:49,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:50,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:51,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:52,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:52,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:53,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:54,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:55,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:48:56,931][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:48:57,885][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:48:57,886][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:48:59,260][__main__][INFO] - Iteration 816 took 55s (38.85% Gen, 61.15% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 54m 36s. Estimated total time: 15h 33m 4s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 18s, 500 more iterations: 7h 46m 32s. +[2025-08-20 20:48:59,262][__main__][INFO] - Starting iteration 816. +[2025-08-20 20:49:22,401][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:49:22,402][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:49:22,408][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:49:24,854][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:49:24,855][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:49:24,862][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:49:24,864][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:49:24,864][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:49:25,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:25,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:26,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:27,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:28,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:29,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:29,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:30,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:31,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:32,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:33,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:33,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:34,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:35,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:36,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:37,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:37,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:38,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:39,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:40,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:41,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:42,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:43,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:43,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:44,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:45,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:46,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:47,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:47,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:48,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:49,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:50,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:49:51,974][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:49:52,885][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:49:52,887][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:49:54,217][__main__][INFO] - Iteration 817 took 54s (37.67% Gen, 62.33% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2h 36m 31s. Estimated total time: 15h 15m 54s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 57s. +[2025-08-20 20:49:54,218][__main__][INFO] - Starting iteration 817. +[2025-08-20 20:50:17,928][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:50:17,929][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:50:17,936][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:50:20,400][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:50:20,402][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:50:20,408][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:50:20,410][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:50:20,411][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:50:20,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:21,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:22,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:23,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:23,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:24,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:25,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:26,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:27,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:27,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:28,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:29,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:30,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:31,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:31,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:32,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:33,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:34,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:34,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:35,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:36,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:37,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:38,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:39,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:40,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:41,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:41,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:42,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:43,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:44,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:45,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:45,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:50:47,492][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:50:48,458][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:50:48,459][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:50:49,852][__main__][INFO] - Iteration 818 took 55s (38.22% Gen, 61.78% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 46m 54s. Estimated total time: 15h 27m 13s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 43s, 500 more iterations: 7h 43m 36s. +[2025-08-20 20:50:49,853][__main__][INFO] - Starting iteration 818. +[2025-08-20 20:51:13,704][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:51:13,705][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:51:13,711][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:51:16,159][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:51:16,161][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:51:16,167][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:51:16,170][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:51:16,170][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:51:16,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:17,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:18,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:18,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:19,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:20,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:21,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:22,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:22,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:23,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:24,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:25,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:26,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:26,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:27,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:28,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:29,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:29,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:30,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:32,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:32,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:33,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:34,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:35,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:35,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:36,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:37,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:38,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:39,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:39,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:40,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:41,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:51:43,191][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:51:44,127][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:51:44,128][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:51:45,461][__main__][INFO] - Iteration 819 took 55s (38.50% Gen, 61.50% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 45m 32s. Estimated total time: 15h 26m 47s. Time estimates for 10 more iterations: 9m 16s, 100 more iterations: 1h 32m 40s, 500 more iterations: 7h 43m 23s. +[2025-08-20 20:51:45,462][__main__][INFO] - Starting iteration 819. +[2025-08-20 20:52:09,003][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:52:09,004][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:52:09,010][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:52:11,488][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:52:11,489][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:52:11,496][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:52:11,498][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:52:11,498][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:52:11,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:12,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:13,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:14,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:14,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:15,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:16,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:17,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:18,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:18,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:19,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:20,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:21,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:22,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:22,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:23,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:24,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:25,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:26,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:26,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:27,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:29,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:29,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:30,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:31,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:32,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:32,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:33,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:34,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:35,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:36,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:36,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:52:38,611][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:52:39,608][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:52:39,610][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:52:41,441][__main__][INFO] - Iteration 820 took 55s (37.64% Gen, 62.36% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 50m 48s. Estimated total time: 15h 32m 58s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 17s, 500 more iterations: 7h 46m 29s. +[2025-08-20 20:52:41,443][__main__][INFO] - Starting iteration 820. +[2025-08-20 20:53:05,176][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:53:05,178][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:53:05,184][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:53:07,618][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:53:07,619][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:53:07,626][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:53:07,628][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:53:07,628][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:53:07,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:08,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:09,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:10,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:11,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:11,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:12,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:13,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:14,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:15,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:15,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:16,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:17,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:18,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:19,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:19,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:20,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:21,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:22,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:23,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:23,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:24,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:25,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:26,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:27,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:28,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:29,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:29,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:30,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:31,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:32,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:33,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:53:34,711][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:53:35,674][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:53:35,675][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:53:37,043][__main__][INFO] - Iteration 821 took 55s (38.31% Gen, 61.69% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 43m 33s. Estimated total time: 15h 26m 39s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 39s, 500 more iterations: 7h 43m 19s. +[2025-08-20 20:53:37,044][__main__][INFO] - Starting iteration 821. +[2025-08-20 20:54:01,149][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:01,150][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:01,156][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:03,631][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:03,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:03,638][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:03,641][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:54:03,641][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:54:03,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:04,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:05,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:06,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:07,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:07,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:08,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:09,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:10,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:11,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:11,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:12,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:13,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:14,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:15,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:15,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:16,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:17,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:18,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:19,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:19,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:20,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:21,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:22,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:23,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:24,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:25,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:25,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:26,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:27,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:28,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:29,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:54:30,655][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:54:31,589][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:54:31,590][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:54:32,991][__main__][INFO] - Iteration 822 took 55s (38.67% Gen, 61.33% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 48m 24s. Estimated total time: 15h 32m 26s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 14s, 500 more iterations: 7h 46m 13s. +[2025-08-20 20:54:32,992][__main__][INFO] - Starting iteration 822. +[2025-08-20 20:54:56,872][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:56,874][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:56,880][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:59,318][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:59,320][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:59,326][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:54:59,328][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:54:59,329][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:54:59,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:00,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:01,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:02,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:02,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:03,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:04,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:05,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:05,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:06,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:07,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:08,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:09,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:09,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:10,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:11,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:12,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:13,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:13,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:14,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:15,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:16,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:17,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:17,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:19,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:20,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:20,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:21,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:22,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:23,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:24,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:24,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:26,433][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:55:27,409][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:55:27,410][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:55:28,709][__main__][INFO] - Iteration 823 took 55s (38.49% Gen, 61.51% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 43m 38s. Estimated total time: 15h 28m 36s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 51s, 500 more iterations: 7h 44m 18s. +[2025-08-20 20:55:28,711][__main__][INFO] - Starting iteration 823. +[2025-08-20 20:55:52,579][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:55:52,581][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:55:52,587][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:55:55,043][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:55:55,045][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:55:55,051][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:55:55,053][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:55:55,053][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:55:55,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:56,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:56,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:57,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:58,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:55:59,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:00,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:00,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:01,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:02,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:03,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:04,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:04,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:05,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:06,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:07,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:08,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:08,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:09,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:10,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:11,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:12,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:12,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:13,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:14,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:15,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:16,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:16,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:17,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:18,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:19,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:20,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:21,760][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:56:22,727][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:56:22,729][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:56:24,269][__main__][INFO] - Iteration 824 took 55s (38.57% Gen, 61.43% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 40m 4s. Estimated total time: 15h 25m 57s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 35s, 500 more iterations: 7h 42m 58s. +[2025-08-20 20:56:24,270][__main__][INFO] - Starting iteration 824. +[2025-08-20 20:56:47,977][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:56:47,979][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:56:47,985][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:56:50,441][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:56:50,443][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:56:50,449][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:56:50,451][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:56:50,452][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:56:50,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:51,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:52,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:53,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:53,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:54,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:55,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:56,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:57,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:57,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:58,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:56:59,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:00,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:01,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:01,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:02,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:03,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:04,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:05,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:05,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:06,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:07,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:08,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:09,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:09,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:10,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:11,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:12,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:13,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:13,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:15,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:15,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:17,438][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:57:18,345][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:57:18,347][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:57:19,686][__main__][INFO] - Iteration 825 took 55s (38.40% Gen, 61.60% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 36m 47s. Estimated total time: 15h 23m 35s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 21s, 500 more iterations: 7h 41m 47s. +[2025-08-20 20:57:19,688][__main__][INFO] - Starting iteration 825. +[2025-08-20 20:57:43,654][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:57:43,656][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:57:43,662][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:57:46,114][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:57:46,115][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:57:46,121][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:57:46,124][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:57:46,124][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:57:46,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:47,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:48,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:48,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:49,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:50,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:51,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:51,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:52,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:53,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:54,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:55,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:55,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:56,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:57,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:58,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:59,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:57:59,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:00,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:01,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:02,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:03,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:04,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:05,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:06,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:06,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:07,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:08,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:09,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:09,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:10,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:11,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:13,179][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:58:14,190][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:58:14,192][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:58:15,490][__main__][INFO] - Iteration 826 took 55s (38.57% Gen, 61.43% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 42m 17s. Estimated total time: 15h 30m 2s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 0s, 500 more iterations: 7h 45m 1s. +[2025-08-20 20:58:15,492][__main__][INFO] - Starting iteration 826. +[2025-08-20 20:58:39,687][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:58:39,689][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:58:39,695][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:58:42,137][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:58:42,138][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:58:42,144][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:58:42,147][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:58:42,147][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:58:42,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:43,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:44,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:44,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:45,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:46,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:47,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:47,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:48,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:49,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:50,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:51,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:51,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:52,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:53,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:54,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:55,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:55,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:56,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:57,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:58,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:59,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:58:59,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:01,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:02,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:02,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:03,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:04,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:05,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:05,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:06,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:07,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:09,175][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 20:59:10,128][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 20:59:10,129][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 20:59:11,567][__main__][INFO] - Iteration 827 took 56s (38.84% Gen, 61.16% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 45m 54s. Estimated total time: 15h 34m 34s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 27s, 500 more iterations: 7h 47m 17s. +[2025-08-20 20:59:11,568][__main__][INFO] - Starting iteration 827. +[2025-08-20 20:59:35,626][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:59:35,627][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:59:35,634][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:59:38,091][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:59:38,093][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:59:38,099][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 20:59:38,101][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 20:59:38,102][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 20:59:38,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:39,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:39,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:40,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:41,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:42,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:43,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:43,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:44,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:45,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:46,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:47,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:47,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:48,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:49,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:50,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:51,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:51,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:52,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:53,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:54,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:55,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:55,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:56,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:57,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:58,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 20:59:59,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:00,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:01,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:01,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:02,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:03,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:05,132][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:00:06,068][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:00:06,069][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:00:09,050][__main__][INFO] - Iteration 828 took 57s (37.59% Gen, 62.41% Train). Generation: 21s, Training: 35s. Estimated remaining time: 3h 8m 22s. Estimated total time: 15h 58m 0s. Time estimates for 10 more iterations: 9m 34s, 100 more iterations: 1h 35m 48s, 500 more iterations: 7h 59m 0s. +[2025-08-20 21:00:09,051][__main__][INFO] - Starting iteration 828. +[2025-08-20 21:00:33,156][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:00:33,157][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:00:33,163][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:00:35,627][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:00:35,628][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:00:35,635][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:00:35,637][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:00:35,637][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:00:35,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:36,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:37,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:38,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:39,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:39,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:40,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:41,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:42,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:43,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:43,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:44,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:45,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:46,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:47,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:47,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:48,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:49,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:50,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:51,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:51,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:52,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:53,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:54,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:55,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:55,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:56,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:57,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:58,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:00:59,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:00,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:01,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:02,625][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:01:03,571][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:01:03,572][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:01:05,728][__main__][INFO] - Iteration 829 took 56s (38.19% Gen, 61.81% Train). Generation: 21s, Training: 35s. Estimated remaining time: 2h 54m 1s. Estimated total time: 15h 44m 36s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 27s, 500 more iterations: 7h 52m 18s. +[2025-08-20 21:01:05,730][__main__][INFO] - Starting iteration 829. +[2025-08-20 21:01:29,933][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:01:29,935][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:01:29,941][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:01:32,400][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:01:32,401][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:01:32,407][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:01:32,410][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:01:32,410][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:01:32,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:33,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:34,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:35,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:35,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:36,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:37,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:38,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:39,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:39,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:40,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:41,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:42,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:43,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:43,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:44,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:45,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:46,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:47,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:47,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:48,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:49,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:50,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:50,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:51,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:53,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:53,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:54,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:55,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:56,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:57,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:57,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:01:59,505][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:02:00,789][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:02:00,791][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:02:02,059][__main__][INFO] - Iteration 830 took 56s (38.59% Gen, 61.41% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 47m 18s. Estimated total time: 15h 38m 49s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 52s, 500 more iterations: 7h 49m 24s. +[2025-08-20 21:02:02,061][__main__][INFO] - Starting iteration 830. +[2025-08-20 21:02:26,060][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:02:26,061][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:02:26,067][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:02:28,524][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:02:28,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:02:28,531][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:02:28,534][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:02:28,534][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:02:28,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:29,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:30,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:31,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:32,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:32,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:33,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:34,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:35,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:35,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:36,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:37,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:38,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:39,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:39,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:40,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:41,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:42,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:43,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:43,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:44,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:45,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:46,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:47,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:47,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:48,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:49,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:50,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:51,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:52,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:53,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:53,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:02:55,510][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:02:56,426][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:02:56,427][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:02:57,804][__main__][INFO] - Iteration 831 took 55s (38.67% Gen, 61.33% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 36m 36s. Estimated total time: 15h 29m 2s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 54s, 500 more iterations: 7h 44m 31s. +[2025-08-20 21:02:57,805][__main__][INFO] - Starting iteration 831. +[2025-08-20 21:03:22,022][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:03:22,023][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:03:22,029][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:03:24,480][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:03:24,481][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:03:24,487][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:03:24,490][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:03:24,490][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:03:24,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:25,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:26,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:27,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:27,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:28,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:29,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:30,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:31,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:31,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:32,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:33,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:34,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:35,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:35,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:36,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:37,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:38,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:39,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:39,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:40,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:41,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:42,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:43,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:44,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:45,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:46,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:46,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:47,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:48,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:49,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:49,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:03:51,578][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:03:52,565][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:03:52,566][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:03:53,816][__main__][INFO] - Iteration 832 took 56s (38.88% Gen, 61.12% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 40m 7s. Estimated total time: 15h 33m 30s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 21s, 500 more iterations: 7h 46m 45s. +[2025-08-20 21:03:53,818][__main__][INFO] - Starting iteration 832. +[2025-08-20 21:04:18,379][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:04:18,380][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:04:18,387][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:04:20,841][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:04:20,842][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:04:20,848][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:04:20,850][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:04:20,851][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:04:21,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:21,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:22,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:23,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:24,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:25,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:25,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:26,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:27,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:28,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:29,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:29,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:30,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:31,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:32,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:33,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:33,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:34,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:35,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:36,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:37,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:37,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:38,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:39,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:40,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:41,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:42,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:43,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:43,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:44,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:45,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:46,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:04:47,955][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:04:48,951][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:04:48,952][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:04:50,306][__main__][INFO] - Iteration 833 took 56s (39.16% Gen, 60.84% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 47m 9s. Estimated total time: 15h 41m 28s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 8s, 500 more iterations: 7h 50m 44s. +[2025-08-20 21:04:50,308][__main__][INFO] - Starting iteration 833. +[2025-08-20 21:05:14,627][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:05:14,628][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:05:14,635][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:05:17,105][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:05:17,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:05:17,113][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:05:17,116][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:05:17,116][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:05:17,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:18,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:19,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:19,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:20,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:21,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:22,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:22,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:23,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:24,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:25,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:26,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:26,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:27,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:28,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:29,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:30,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:30,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:31,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:32,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:33,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:34,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:34,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:35,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:36,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:37,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:38,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:39,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:40,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:40,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:41,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:42,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:05:44,062][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:05:44,968][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:05:44,969][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:05:47,338][__main__][INFO] - Iteration 834 took 57s (38.33% Gen, 61.67% Train). Generation: 21s, Training: 35s. Estimated remaining time: 2h 55m 13s. Estimated total time: 15h 50m 29s. Time estimates for 10 more iterations: 9m 30s, 100 more iterations: 1h 35m 2s, 500 more iterations: 7h 55m 14s. +[2025-08-20 21:05:47,340][__main__][INFO] - Starting iteration 834. +[2025-08-20 21:06:11,626][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:06:11,627][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:06:11,633][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:06:14,093][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:06:14,094][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:06:14,101][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:06:14,103][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:06:14,103][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:06:14,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:15,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:15,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:16,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:17,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:18,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:19,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:19,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:20,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:21,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:22,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:23,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:23,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:24,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:25,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:26,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:27,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:27,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:28,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:29,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:30,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:31,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:31,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:32,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:33,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:34,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:35,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:36,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:37,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:37,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:38,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:39,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:06:41,096][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:06:42,063][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:06:42,065][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:06:43,408][__main__][INFO] - Iteration 835 took 56s (38.90% Gen, 61.10% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 38m 15s. Estimated total time: 15h 34m 28s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 26s, 500 more iterations: 7h 47m 14s. +[2025-08-20 21:06:43,410][__main__][INFO] - Starting iteration 835. +[2025-08-20 21:07:07,835][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:07:07,836][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:07:07,843][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:07:10,298][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:07:10,300][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:07:10,306][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:07:10,308][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:07:10,309][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:07:10,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:11,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:12,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:12,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:13,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:14,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:15,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:16,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:16,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:17,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:18,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:19,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:20,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:20,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:21,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:22,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:23,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:24,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:24,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:25,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:26,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:27,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:28,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:28,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:29,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:30,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:31,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:32,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:32,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:34,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:34,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:35,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:07:37,397][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:07:38,355][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:07:38,356][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:07:39,609][__main__][INFO] - Iteration 836 took 56s (39.08% Gen, 60.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 39m 30s. Estimated total time: 15h 36m 38s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 39s, 500 more iterations: 7h 48m 19s. +[2025-08-20 21:07:39,610][__main__][INFO] - Starting iteration 836. +[2025-08-20 21:08:03,982][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:08:03,983][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:08:03,989][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:08:06,441][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:08:06,442][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:08:06,449][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:08:06,451][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:08:06,451][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:08:06,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:07,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:08,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:09,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:09,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:10,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:11,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:12,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:13,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:13,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:14,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:15,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:16,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:17,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:17,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:18,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:19,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:20,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:21,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:21,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:22,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:23,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:24,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:25,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:26,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:27,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:27,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:28,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:29,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:30,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:31,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:31,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:08:33,377][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:08:34,354][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:08:34,356][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:08:35,735][__main__][INFO] - Iteration 837 took 56s (39.05% Gen, 60.95% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 37m 19s. Estimated total time: 15h 35m 23s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 32s, 500 more iterations: 7h 47m 41s. +[2025-08-20 21:08:35,737][__main__][INFO] - Starting iteration 837. +[2025-08-20 21:09:00,169][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:00,171][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:00,177][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:02,646][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:02,647][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:02,653][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:02,656][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:09:02,656][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:09:02,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:03,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:04,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:05,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:06,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:06,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:07,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:08,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:09,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:10,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:10,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:11,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:12,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:13,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:14,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:14,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:15,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:16,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:17,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:18,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:18,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:19,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:20,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:21,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:22,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:22,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:24,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:24,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:25,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:26,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:27,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:28,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:09:29,663][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:09:30,545][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:09:30,547][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:09:31,940][__main__][INFO] - Iteration 838 took 56s (39.11% Gen, 60.89% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 37m 41s. Estimated total time: 15h 36m 42s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 40s, 500 more iterations: 7h 48m 21s. +[2025-08-20 21:09:31,941][__main__][INFO] - Starting iteration 838. +[2025-08-20 21:09:56,802][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:56,803][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:56,810][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:59,320][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:59,322][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:59,329][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:09:59,331][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:09:59,332][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:09:59,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:00,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:01,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:02,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:02,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:03,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:04,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:05,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:05,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:06,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:07,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:08,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:09,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:09,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:10,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:11,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:12,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:13,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:13,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:14,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:15,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:16,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:17,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:17,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:18,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:19,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:20,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:21,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:22,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:23,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:23,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:24,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:26,370][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:10:27,267][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:10:27,269][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:10:28,824][__main__][INFO] - Iteration 839 took 56s (39.32% Gen, 60.68% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 48m 4s. Estimated total time: 15h 48m 2s. Time estimates for 10 more iterations: 9m 28s, 100 more iterations: 1h 34m 48s, 500 more iterations: 7h 54m 1s. +[2025-08-20 21:10:28,826][__main__][INFO] - Starting iteration 839. +[2025-08-20 21:10:53,192][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:10:53,193][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:10:53,199][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:10:55,658][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:10:55,659][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:10:55,666][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:10:55,668][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:10:55,669][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:10:55,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:56,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:57,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:58,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:59,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:10:59,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:00,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:01,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:02,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:03,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:03,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:04,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:05,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:06,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:07,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:07,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:08,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:09,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:10,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:11,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:11,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:12,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:13,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:14,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:15,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:16,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:17,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:17,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:18,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:19,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:20,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:21,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:22,664][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:11:23,663][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:11:23,665][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:11:25,086][__main__][INFO] - Iteration 840 took 56s (38.95% Gen, 61.05% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 36m 46s. Estimated total time: 15h 37m 40s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 46s, 500 more iterations: 7h 48m 50s. +[2025-08-20 21:11:25,088][__main__][INFO] - Starting iteration 840. +[2025-08-20 21:11:49,572][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:11:49,573][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:11:49,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:11:52,029][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:11:52,030][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:11:52,037][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:11:52,039][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:11:52,040][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:11:52,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:53,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:53,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:54,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:55,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:56,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:57,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:57,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:58,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:11:59,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:00,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:01,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:01,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:02,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:03,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:04,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:05,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:05,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:06,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:07,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:08,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:09,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:09,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:10,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:11,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:12,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:12,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:13,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:14,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:15,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:16,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:16,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:18,621][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:12:19,555][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:12:19,556][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:12:20,815][__main__][INFO] - Iteration 841 took 55s (39.56% Gen, 60.44% Train). Generation: 22s, Training: 33s. Estimated remaining time: 2h 26m 57s. Estimated total time: 15h 28m 46s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 52s, 500 more iterations: 7h 44m 23s. +[2025-08-20 21:12:20,818][__main__][INFO] - Starting iteration 841. +[2025-08-20 21:12:45,319][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:12:45,320][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:12:45,326][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:12:47,774][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:12:47,776][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:12:47,782][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:12:47,784][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:12:47,785][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:12:48,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:48,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:49,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:50,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:51,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:52,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:52,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:53,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:54,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:55,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:55,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:56,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:57,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:58,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:59,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:12:59,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:00,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:01,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:02,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:03,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:03,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:04,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:05,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:06,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:07,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:07,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:08,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:09,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:10,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:11,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:11,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:12,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:14,352][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:13:15,285][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:13:15,286][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:13:16,629][__main__][INFO] - Iteration 842 took 55s (39.50% Gen, 60.50% Train). Generation: 22s, Training: 33s. Estimated remaining time: 2h 27m 25s. Estimated total time: 15h 30m 11s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 1s, 500 more iterations: 7h 45m 5s. +[2025-08-20 21:13:16,630][__main__][INFO] - Starting iteration 842. +[2025-08-20 21:13:41,085][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:13:41,086][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:13:41,092][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:13:43,566][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:13:43,568][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:13:43,574][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:13:43,576][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:13:43,577][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:13:43,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:44,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:45,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:46,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:47,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:47,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:48,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:49,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:50,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:51,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:51,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:52,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:53,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:54,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:54,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:55,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:56,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:57,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:58,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:13:59,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:00,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:01,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:01,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:02,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:03,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:04,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:05,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:05,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:06,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:07,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:08,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:08,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:10,565][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:14:11,539][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:14:11,540][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:14:12,948][__main__][INFO] - Iteration 843 took 56s (39.03% Gen, 60.97% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 34m 55s. Estimated total time: 15h 38m 37s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 51s, 500 more iterations: 7h 49m 18s. +[2025-08-20 21:14:12,950][__main__][INFO] - Starting iteration 843. +[2025-08-20 21:14:37,739][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:14:37,740][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:14:37,746][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:14:40,192][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:14:40,193][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:14:40,199][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:14:40,201][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:14:40,202][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:14:40,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:41,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:42,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:42,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:43,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:44,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:45,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:46,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:46,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:47,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:48,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:49,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:50,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:50,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:51,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:52,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:53,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:54,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:54,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:55,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:56,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:57,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:57,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:58,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:14:59,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:00,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:01,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:01,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:02,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:04,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:04,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:05,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:07,244][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:15:08,151][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:15:08,152][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:15:09,446][__main__][INFO] - Iteration 844 took 56s (39.55% Gen, 60.44% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 36m 57s. Estimated total time: 15h 41m 35s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 9s, 500 more iterations: 7h 50m 47s. +[2025-08-20 21:15:09,447][__main__][INFO] - Starting iteration 844. +[2025-08-20 21:15:33,857][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:15:33,858][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:15:33,864][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:15:36,294][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:15:36,295][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:15:36,302][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:15:36,304][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:15:36,304][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:15:36,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:37,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:38,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:38,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:39,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:40,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:41,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:42,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:42,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:43,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:44,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:45,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:46,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:46,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:47,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:48,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:49,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:50,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:50,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:51,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:52,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:53,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:54,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:54,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:55,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:56,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:57,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:58,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:15:59,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:00,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:00,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:01,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:03,344][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:16:04,324][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:16:04,326][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:16:05,686][__main__][INFO] - Iteration 845 took 56s (39.09% Gen, 60.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 31m 43s. Estimated total time: 15h 37m 18s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 43s, 500 more iterations: 7h 48m 39s. +[2025-08-20 21:16:05,687][__main__][INFO] - Starting iteration 845. +[2025-08-20 21:16:30,181][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:16:30,182][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:16:30,188][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:16:32,621][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:16:32,623][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:16:32,629][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:16:32,631][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:16:32,632][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:16:32,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:33,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:34,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:35,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:36,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:36,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:37,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:38,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:39,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:40,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:40,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:41,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:42,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:43,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:44,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:44,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:45,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:46,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:47,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:48,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:48,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:49,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:50,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:51,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:51,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:52,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:53,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:54,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:55,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:56,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:57,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:58,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:16:59,622][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:17:00,581][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:17:00,582][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:17:01,942][__main__][INFO] - Iteration 846 took 56s (39.19% Gen, 60.80% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 31m 3s. Estimated total time: 15h 37m 34s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 45s, 500 more iterations: 7h 48m 47s. +[2025-08-20 21:17:01,943][__main__][INFO] - Starting iteration 846. +[2025-08-20 21:17:26,342][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:17:26,344][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:17:26,350][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:17:28,808][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:17:28,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:17:28,815][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:17:28,817][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:17:28,818][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:17:29,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:29,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:30,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:31,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:32,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:33,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:33,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:34,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:35,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:36,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:37,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:37,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:38,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:39,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:40,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:41,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:41,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:42,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:43,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:44,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:44,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:45,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:46,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:47,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:48,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:48,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:49,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:50,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:51,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:52,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:53,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:54,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:17:55,893][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:17:56,792][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:17:56,794][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:17:58,041][__main__][INFO] - Iteration 847 took 56s (39.13% Gen, 60.87% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 27m 30s. Estimated total time: 15h 34m 57s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 29s, 500 more iterations: 7h 47m 28s. +[2025-08-20 21:17:58,042][__main__][INFO] - Starting iteration 847. +[2025-08-20 21:18:22,454][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:18:22,455][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:18:22,462][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:18:24,883][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:18:24,884][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:18:24,890][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:18:24,893][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:18:24,893][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:18:25,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:25,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:26,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:27,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:28,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:29,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:29,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:30,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:31,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:32,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:33,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:33,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:34,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:35,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:36,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:37,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:37,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:38,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:39,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:40,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:41,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:41,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:42,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:43,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:44,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:45,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:46,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:47,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:47,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:48,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:49,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:50,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:18:51,920][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:18:52,922][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:18:52,924][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:18:54,290][__main__][INFO] - Iteration 848 took 56s (39.09% Gen, 60.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 29m 4s. Estimated total time: 15h 37m 27s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 44s, 500 more iterations: 7h 48m 43s. +[2025-08-20 21:18:54,292][__main__][INFO] - Starting iteration 848. +[2025-08-20 21:19:19,024][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:19:19,025][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:19:19,031][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:19:21,491][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:19:21,492][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:19:21,498][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:19:21,500][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:19:21,501][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:19:21,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:22,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:23,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:24,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:24,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:25,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:26,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:27,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:28,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:28,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:29,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:30,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:31,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:32,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:32,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:33,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:34,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:35,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:36,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:36,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:37,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:38,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:39,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:40,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:40,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:42,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:42,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:43,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:44,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:45,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:46,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:46,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:19:48,533][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:19:49,448][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:19:49,450][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:19:50,845][__main__][INFO] - Iteration 849 took 56s (39.40% Gen, 60.60% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 33m 12s. Estimated total time: 15h 42m 32s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 15s, 500 more iterations: 7h 51m 16s. +[2025-08-20 21:19:50,846][__main__][INFO] - Starting iteration 849. +[2025-08-20 21:20:15,250][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:20:15,251][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:20:15,258][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:20:17,722][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:20:17,724][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:20:17,730][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:20:17,732][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:20:17,733][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:20:18,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:18,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:19,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:20,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:21,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:21,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:22,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:23,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:24,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:25,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:25,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:26,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:27,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:28,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:29,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:29,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:30,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:31,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:32,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:33,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:33,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:34,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:35,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:36,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:37,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:37,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:38,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:40,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:40,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:41,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:42,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:43,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:20:44,892][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:20:45,885][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:20:45,887][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:20:47,117][__main__][INFO] - Iteration 850 took 56s (39.01% Gen, 60.98% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 27m 34s. Estimated total time: 15h 37m 50s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 47s, 500 more iterations: 7h 48m 55s. +[2025-08-20 21:20:47,118][__main__][INFO] - Starting iteration 850. +[2025-08-20 21:21:11,528][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:21:11,530][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:21:11,536][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:21:13,978][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:21:13,980][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:21:13,986][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:21:13,988][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:21:13,989][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:21:14,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:15,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:15,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:16,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:17,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:18,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:19,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:19,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:20,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:21,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:22,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:23,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:23,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:24,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:25,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:26,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:26,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:27,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:28,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:29,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:30,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:30,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:31,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:32,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:33,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:34,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:35,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:36,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:36,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:37,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:38,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:39,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:21:41,093][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:21:42,001][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:21:42,002][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:21:45,818][__main__][INFO] - Iteration 851 took 58s (37.43% Gen, 58.28% Train). Generation: 21s, Training: 34s. Estimated remaining time: 3h 7m 4s. Estimated total time: 16h 18m 19s. Time estimates for 10 more iterations: 9m 46s, 100 more iterations: 1h 37m 49s, 500 more iterations: 8h 9m 9s. +[2025-08-20 21:21:45,820][__main__][INFO] - Starting iteration 851. +[2025-08-20 21:22:10,133][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:22:10,135][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:22:10,141][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:22:12,607][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:22:12,608][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:22:12,615][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:22:12,617][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:22:12,617][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:22:12,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:13,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:14,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:15,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:16,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:16,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:17,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:18,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:19,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:20,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:20,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:21,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:22,423][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:23,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:24,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:24,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:25,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:26,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:27,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:27,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:28,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:29,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:30,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:31,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:31,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:32,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:33,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:34,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:35,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:36,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:37,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:37,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:22:39,602][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:22:40,533][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:22:40,534][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:22:42,051][__main__][INFO] - Iteration 852 took 56s (38.87% Gen, 61.13% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 24m 59s. Estimated total time: 15h 37m 10s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 43s, 500 more iterations: 7h 48m 35s. +[2025-08-20 21:22:42,052][__main__][INFO] - Starting iteration 852. +[2025-08-20 21:23:06,285][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:23:06,286][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:23:06,293][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:23:08,737][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:23:08,738][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:23:08,744][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:23:08,747][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:23:08,747][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:23:09,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:09,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:10,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:11,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:12,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:13,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:13,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:14,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:15,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:16,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:16,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:17,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:18,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:19,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:20,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:20,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:21,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:22,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:23,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:24,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:25,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:26,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:27,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:27,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:28,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:29,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:30,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:31,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:31,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:32,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:33,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:34,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:23:35,856][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:23:36,804][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:23:36,806][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:23:38,172][__main__][INFO] - Iteration 853 took 56s (38.83% Gen, 61.17% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 22m 12s. Estimated total time: 15h 35m 19s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 31s, 500 more iterations: 7h 47m 39s. +[2025-08-20 21:23:38,174][__main__][INFO] - Starting iteration 853. +[2025-08-20 21:24:02,827][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:02,829][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:02,835][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:05,275][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:05,276][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:05,282][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:05,284][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:24:05,285][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:24:05,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:06,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:07,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:07,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:08,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:09,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:10,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:11,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:11,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:12,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:13,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:14,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:15,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:15,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:16,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:17,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:18,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:19,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:19,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:20,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:21,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:22,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:23,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:23,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:24,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:25,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:26,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:27,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:28,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:29,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:29,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:30,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:24:32,326][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:24:33,352][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:24:33,354][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:24:34,686][__main__][INFO] - Iteration 854 took 56s (39.30% Gen, 60.70% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 27m 47s. Estimated total time: 15h 41m 51s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 11s, 500 more iterations: 7h 50m 55s. +[2025-08-20 21:24:34,687][__main__][INFO] - Starting iteration 854. +[2025-08-20 21:24:58,953][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:58,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:24:58,961][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:01,411][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:01,413][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:01,419][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:01,421][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:25:01,422][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:25:01,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:02,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:03,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:04,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:04,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:05,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:06,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:07,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:08,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:08,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:09,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:10,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:11,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:12,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:12,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:13,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:14,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:15,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:16,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:16,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:17,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:18,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:19,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:20,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:21,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:22,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:22,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:23,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:24,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:25,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:26,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:26,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:28,444][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:25:29,375][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:25:29,376][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:25:30,694][__main__][INFO] - Iteration 855 took 56s (38.95% Gen, 61.05% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 18m 26s. Estimated total time: 15h 33m 26s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 20s, 500 more iterations: 7h 46m 43s. +[2025-08-20 21:25:30,695][__main__][INFO] - Starting iteration 855. +[2025-08-20 21:25:54,997][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:54,998][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:55,004][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:57,451][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:57,453][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:57,459][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:25:57,461][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:25:57,462][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:25:57,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:58,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:25:59,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:00,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:00,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:01,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:02,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:03,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:04,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:04,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:05,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:06,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:07,298][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:08,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:08,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:09,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:10,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:11,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:12,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:12,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:13,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:14,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:15,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:16,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:16,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:17,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:18,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:19,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:20,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:20,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:21,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:22,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:24,519][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:26:25,422][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:26:25,423][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:26:26,728][__main__][INFO] - Iteration 856 took 56s (38.99% Gen, 61.01% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 17m 56s. Estimated total time: 15h 33m 52s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 23s, 500 more iterations: 7h 46m 56s. +[2025-08-20 21:26:26,729][__main__][INFO] - Starting iteration 856. +[2025-08-20 21:26:50,894][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:26:50,895][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:26:50,902][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:26:53,364][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:26:53,365][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:26:53,372][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:26:53,374][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:26:53,374][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:26:53,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:54,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:55,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:56,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:56,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:57,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:58,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:26:59,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:00,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:00,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:01,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:02,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:03,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:04,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:04,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:05,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:06,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:07,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:07,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:08,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:09,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:10,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:11,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:11,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:12,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:13,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:14,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:15,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:15,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:17,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:18,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:18,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:20,525][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:27:21,502][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:27:21,503][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:27:22,802][__main__][INFO] - Iteration 857 took 56s (38.75% Gen, 61.25% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 17m 40s. Estimated total time: 15h 34m 32s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 27s, 500 more iterations: 7h 47m 16s. +[2025-08-20 21:27:22,804][__main__][INFO] - Starting iteration 857. +[2025-08-20 21:27:46,986][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:27:46,988][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:27:46,994][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:27:49,447][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:27:49,448][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:27:49,454][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:27:49,457][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:27:49,457][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:27:49,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:50,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:51,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:52,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:52,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:53,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:54,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:55,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:56,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:56,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:57,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:58,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:27:59,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:00,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:00,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:01,678][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:02,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:03,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:04,066][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:04,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:05,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:06,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:07,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:08,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:08,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:10,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:10,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:11,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:12,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:13,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:14,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:14,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:16,401][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:28:17,283][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:28:17,285][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:28:18,662][__main__][INFO] - Iteration 858 took 55s (38.93% Gen, 61.07% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 13m 10s. Estimated total time: 15h 30m 57s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 5s, 500 more iterations: 7h 45m 28s. +[2025-08-20 21:28:18,663][__main__][INFO] - Starting iteration 858. +[2025-08-20 21:28:43,313][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:28:43,315][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:28:43,322][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:28:45,777][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:28:45,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:28:45,785][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:28:45,787][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:28:45,788][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:28:46,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:46,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:47,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:48,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:49,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:50,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:50,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:51,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:52,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:53,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:54,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:54,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:55,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:56,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:57,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:57,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:58,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:28:59,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:00,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:01,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:02,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:03,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:04,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:04,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:05,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:06,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:07,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:08,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:08,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:09,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:10,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:11,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:12,957][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:29:13,935][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:29:13,937][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:29:15,381][__main__][INFO] - Iteration 859 took 56s (39.15% Gen, 60.85% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 26m 33s. Estimated total time: 15h 45m 17s. Time estimates for 10 more iterations: 9m 27s, 100 more iterations: 1h 34m 31s, 500 more iterations: 7h 52m 38s. +[2025-08-20 21:29:15,385][__main__][INFO] - Starting iteration 859. +[2025-08-20 21:29:39,683][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:29:39,684][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:29:39,691][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:29:42,150][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:29:42,152][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:29:42,158][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:29:42,160][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:29:42,161][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:29:42,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:43,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:44,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:44,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:45,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:46,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:47,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:48,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:48,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:49,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:50,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:51,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:52,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:52,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:53,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:54,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:55,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:55,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:56,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:57,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:58,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:59,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:29:59,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:00,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:01,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:02,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:03,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:03,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:04,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:05,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:06,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:07,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:09,224][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:30:10,173][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:30:10,174][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:30:11,544][__main__][INFO] - Iteration 860 took 56s (38.88% Gen, 61.11% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 16m 12s. Estimated total time: 15h 35m 52s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 35s, 500 more iterations: 7h 47m 56s. +[2025-08-20 21:30:11,545][__main__][INFO] - Starting iteration 860. +[2025-08-20 21:30:35,851][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:30:35,852][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:30:35,858][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:30:38,300][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:30:38,301][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:30:38,307][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:30:38,310][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:30:38,310][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:30:38,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:39,401][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:40,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:40,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:41,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:42,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:43,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:44,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:44,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:45,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:46,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:47,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:48,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:48,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:49,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:50,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:51,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:52,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:52,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:53,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:54,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:55,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:56,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:56,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:57,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:58,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:30:59,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:00,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:00,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:02,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:02,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:03,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:05,231][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:31:06,191][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:31:06,193][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:31:07,758][__main__][INFO] - Iteration 861 took 56s (38.89% Gen, 61.11% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 16m 15s. Estimated total time: 15h 36m 51s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 41s, 500 more iterations: 7h 48m 25s. +[2025-08-20 21:31:07,759][__main__][INFO] - Starting iteration 861. +[2025-08-20 21:31:32,119][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:31:32,121][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:31:32,127][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:31:34,578][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:31:34,580][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:31:34,586][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:31:34,589][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:31:34,589][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:31:34,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:35,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:36,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:37,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:38,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:38,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:39,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:40,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:41,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:42,048][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:42,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:43,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:44,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:45,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:46,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:46,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:47,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:48,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:49,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:50,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:50,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:51,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:52,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:53,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:53,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:54,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:56,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:56,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:57,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:58,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:31:59,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:00,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:01,608][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:32:02,519][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:32:02,520][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:32:03,881][__main__][INFO] - Iteration 862 took 56s (39.03% Gen, 60.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 13m 48s. Estimated total time: 15h 35m 21s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 32s, 500 more iterations: 7h 47m 40s. +[2025-08-20 21:32:03,882][__main__][INFO] - Starting iteration 862. +[2025-08-20 21:32:28,001][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:32:28,003][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:32:28,009][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:32:30,443][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:32:30,445][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:32:30,451][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:32:30,453][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:32:30,454][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:32:30,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:31,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:32,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:33,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:33,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:34,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:35,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:36,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:37,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:37,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:38,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:39,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:40,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:41,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:41,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:42,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:43,473][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:44,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:45,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:45,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:46,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:47,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:48,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:49,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:50,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:51,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:51,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:52,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:53,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:54,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:55,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:55,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:32:57,449][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:32:58,348][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:32:58,350][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:32:59,659][__main__][INFO] - Iteration 863 took 55s (38.90% Gen, 61.10% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 7m 7s. Estimated total time: 15h 29m 36s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 57s, 500 more iterations: 7h 44m 48s. +[2025-08-20 21:32:59,660][__main__][INFO] - Starting iteration 863. +[2025-08-20 21:33:24,284][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:33:24,285][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:33:24,292][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:33:26,748][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:33:26,750][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:33:26,756][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:33:26,758][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:33:26,759][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:33:27,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:27,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:28,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:29,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:30,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:31,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:31,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:32,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:33,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:34,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:35,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:35,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:36,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:37,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:38,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:38,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:39,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:40,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:41,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:42,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:42,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:43,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:44,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:45,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:46,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:46,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:47,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:48,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:49,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:50,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:51,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:52,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:33:53,785][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:33:54,690][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:33:54,692][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:33:56,177][__main__][INFO] - Iteration 864 took 56s (39.22% Gen, 60.78% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 18m 31s. Estimated total time: 15h 41m 56s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 11s, 500 more iterations: 7h 50m 58s. +[2025-08-20 21:33:56,178][__main__][INFO] - Starting iteration 864. +[2025-08-20 21:34:20,557][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:34:20,558][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:34:20,564][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:34:23,020][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:34:23,021][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:34:23,027][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:34:23,030][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:34:23,030][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:34:23,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:24,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:24,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:25,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:26,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:27,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:28,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:28,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:29,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:30,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:31,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:32,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:32,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:33,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:34,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:35,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:36,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:36,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:37,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:38,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:39,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:40,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:41,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:42,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:42,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:43,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:44,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:45,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:46,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:46,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:47,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:48,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:34:50,112][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:34:51,094][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:34:51,096][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:34:52,336][__main__][INFO] - Iteration 865 took 56s (39.03% Gen, 60.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 11m 36s. Estimated total time: 15h 35m 57s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 35s, 500 more iterations: 7h 47m 58s. +[2025-08-20 21:34:52,340][__main__][INFO] - Starting iteration 865. +[2025-08-20 21:35:16,626][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:35:16,628][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:35:16,646][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:35:19,093][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:35:19,094][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:35:19,101][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:35:19,104][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:35:19,104][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:35:19,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:20,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:20,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:21,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:22,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:23,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:24,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:24,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:25,761][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:26,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:27,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:28,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:28,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:29,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:30,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:31,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:32,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:32,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:33,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:34,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:35,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:36,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:36,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:37,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:38,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:39,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:40,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:40,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:41,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:42,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:43,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:44,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:35:46,108][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:35:47,029][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:35:47,030][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:35:48,338][__main__][INFO] - Iteration 866 took 55s (39.00% Gen, 61.00% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 7m 59s. Estimated total time: 15h 33m 16s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 19s, 500 more iterations: 7h 46m 38s. +[2025-08-20 21:35:48,339][__main__][INFO] - Starting iteration 866. +[2025-08-20 21:36:12,715][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:36:12,716][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:36:12,723][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:36:15,195][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:36:15,196][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:36:15,203][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:36:15,205][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:36:15,206][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:36:15,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:16,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:17,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:17,884][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:18,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:19,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:20,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:21,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:21,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:22,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:23,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:24,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:25,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:25,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:26,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:27,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:28,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:29,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:29,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:30,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:31,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:32,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:32,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:33,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:34,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:35,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:36,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:37,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:38,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:38,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:39,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:40,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:36:42,162][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:36:43,061][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:36:43,063][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:36:44,478][__main__][INFO] - Iteration 867 took 56s (39.03% Gen, 60.97% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 9m 25s. Estimated total time: 15h 35m 38s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 33s, 500 more iterations: 7h 47m 49s. +[2025-08-20 21:36:44,480][__main__][INFO] - Starting iteration 867. +[2025-08-20 21:37:08,820][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:37:08,821][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:37:08,827][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:37:11,294][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:37:11,295][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:37:11,302][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:37:11,304][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:37:11,305][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:37:11,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:12,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:13,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:13,986][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:14,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:15,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:16,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:17,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:17,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:18,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:19,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:20,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:21,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:21,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:22,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:23,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:24,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:25,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:25,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:26,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:27,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:28,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:29,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:29,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:30,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:31,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:32,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:33,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:34,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:35,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:35,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:36,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:37:38,361][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:37:39,283][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:37:39,285][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:37:40,743][__main__][INFO] - Iteration 868 took 56s (38.87% Gen, 61.12% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 10m 32s. Estimated total time: 15h 37m 42s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 46s, 500 more iterations: 7h 48m 51s. +[2025-08-20 21:37:40,744][__main__][INFO] - Starting iteration 868. +[2025-08-20 21:38:05,371][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:38:05,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:38:05,379][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:38:07,817][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:38:07,818][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:38:07,825][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:38:07,827][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:38:07,828][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:38:08,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:08,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:09,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:10,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:11,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:12,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:12,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:13,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:14,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:15,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:16,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:16,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:17,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:18,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:19,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:20,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:20,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:21,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:22,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:23,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:24,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:25,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:26,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:26,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:27,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:28,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:29,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:30,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:30,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:31,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:32,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:33,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:38:34,835][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:38:35,800][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:38:35,801][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:38:37,094][__main__][INFO] - Iteration 869 took 56s (39.36% Gen, 60.64% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 11m 3s. Estimated total time: 15h 39m 9s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 54s, 500 more iterations: 7h 49m 34s. +[2025-08-20 21:38:37,096][__main__][INFO] - Starting iteration 869. +[2025-08-20 21:39:01,502][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:01,504][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:01,510][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:03,971][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:03,972][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:03,979][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:03,981][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:39:03,982][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:39:04,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:05,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:05,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:06,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:07,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:08,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:09,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:09,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:10,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:11,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:12,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:13,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:13,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:14,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:15,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:16,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:16,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:17,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:18,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:19,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:20,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:20,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:21,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:22,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:23,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:24,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:24,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:25,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:26,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:27,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:28,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:29,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:39:30,985][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:39:31,892][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:39:31,894][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:39:33,283][__main__][INFO] - Iteration 870 took 56s (39.04% Gen, 60.96% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 7m 24s. Estimated total time: 15h 36m 26s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 38s, 500 more iterations: 7h 48m 13s. +[2025-08-20 21:39:33,285][__main__][INFO] - Starting iteration 870. +[2025-08-20 21:39:57,665][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:57,667][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:39:57,673][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:00,131][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:00,133][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:00,139][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:00,141][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:40:00,142][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:40:00,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:01,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:02,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:02,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:03,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:04,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:05,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:05,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:06,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:07,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:08,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:09,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:09,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:10,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:11,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:12,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:13,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:13,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:14,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:15,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:16,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:17,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:17,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:18,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:19,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:20,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:21,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:21,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:23,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:24,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:24,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:25,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:27,238][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:40:28,148][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:40:28,149][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:40:30,548][__main__][INFO] - Iteration 871 took 57s (38.26% Gen, 61.74% Train). Generation: 21s, Training: 35s. Estimated remaining time: 2h 24m 23s. Estimated total time: 15h 54m 22s. Time estimates for 10 more iterations: 9m 32s, 100 more iterations: 1h 35m 26s, 500 more iterations: 7h 57m 11s. +[2025-08-20 21:40:30,549][__main__][INFO] - Starting iteration 871. +[2025-08-20 21:40:55,121][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:55,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:55,128][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:57,571][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:57,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:57,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:40:57,581][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:40:57,581][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:40:57,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:58,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:40:59,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:00,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:01,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:01,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:02,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:03,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:04,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:05,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:05,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:06,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:07,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:08,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:08,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:09,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:10,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:11,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:12,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:12,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:13,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:14,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:15,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:16,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:17,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:18,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:18,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:19,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:20,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:21,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:22,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:22,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:24,544][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:41:25,506][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:41:25,507][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:41:26,854][__main__][INFO] - Iteration 872 took 56s (39.26% Gen, 60.73% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 7m 28s. Estimated total time: 15h 38m 24s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 50s, 500 more iterations: 7h 49m 12s. +[2025-08-20 21:41:26,856][__main__][INFO] - Starting iteration 872. +[2025-08-20 21:41:51,276][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:41:51,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:41:51,284][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:41:53,754][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:41:53,755][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:41:53,761][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:41:53,763][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:41:53,764][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:41:54,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:54,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:55,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:56,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:57,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:58,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:58,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:41:59,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:00,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:01,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:01,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:02,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:03,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:04,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:05,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:05,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:06,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:07,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:08,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:09,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:10,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:11,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:11,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:12,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:13,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:14,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:15,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:15,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:16,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:17,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:18,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:19,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:20,728][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:42:21,678][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:42:21,680][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:42:23,083][__main__][INFO] - Iteration 873 took 56s (39.06% Gen, 60.94% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 5m 15s. Estimated total time: 15h 37m 7s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 42s, 500 more iterations: 7h 48m 33s. +[2025-08-20 21:42:23,085][__main__][INFO] - Starting iteration 873. +[2025-08-20 21:42:47,476][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:42:47,477][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:42:47,483][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:42:49,963][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:42:49,964][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:42:49,971][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:42:49,973][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:42:49,973][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:42:50,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:51,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:51,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:52,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:53,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:54,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:55,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:55,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:56,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:57,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:58,204][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:58,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:42:59,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:00,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:01,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:02,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:02,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:03,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:04,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:05,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:06,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:06,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:07,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:08,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:09,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:10,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:11,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:12,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:13,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:13,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:14,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:15,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:17,045][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:43:18,019][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:43:18,020][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:43:19,377][__main__][INFO] - Iteration 874 took 56s (38.95% Gen, 61.05% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 5m 23s. Estimated total time: 15h 38m 11s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 49s, 500 more iterations: 7h 49m 5s. +[2025-08-20 21:43:19,378][__main__][INFO] - Starting iteration 874. +[2025-08-20 21:43:44,091][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:43:44,093][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:43:44,099][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:43:46,584][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:43:46,586][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:43:46,592][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:43:46,594][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:43:46,595][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:43:46,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:47,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:48,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:49,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:50,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:50,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:51,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:52,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:53,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:54,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:54,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:55,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:56,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:57,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:58,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:58,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:43:59,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:00,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:01,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:01,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:02,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:03,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:04,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:05,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:05,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:06,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:07,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:08,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:09,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:10,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:11,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:11,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:13,532][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:44:14,514][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:44:14,516][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:44:15,893][__main__][INFO] - Iteration 875 took 56s (39.35% Gen, 60.65% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 8m 9s. Estimated total time: 15h 41m 54s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 11s, 500 more iterations: 7h 50m 57s. +[2025-08-20 21:44:15,895][__main__][INFO] - Starting iteration 875. +[2025-08-20 21:44:40,276][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:44:40,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:44:40,283][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:44:42,724][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:44:42,726][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:44:42,732][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:44:42,734][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:44:42,735][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:44:43,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:43,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:44,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:45,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:46,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:46,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:47,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:48,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:49,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:50,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:50,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:51,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:52,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:53,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:54,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:54,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:55,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:56,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:57,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:58,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:58,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:44:59,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:00,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:01,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:02,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:02,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:03,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:04,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:05,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:06,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:07,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:08,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:09,815][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:45:10,813][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:45:10,815][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:45:12,270][__main__][INFO] - Iteration 876 took 56s (38.92% Gen, 61.08% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 4m 53s. Estimated total time: 15h 39m 34s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 57s, 500 more iterations: 7h 49m 47s. +[2025-08-20 21:45:12,271][__main__][INFO] - Starting iteration 876. +[2025-08-20 21:45:36,674][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:45:36,675][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:45:36,681][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:45:39,129][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:45:39,130][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:45:39,136][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:45:39,139][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:45:39,139][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:45:39,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:40,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:41,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:41,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:42,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:43,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:44,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:44,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:45,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:46,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:47,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:48,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:48,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:49,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:50,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:51,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:52,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:52,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:53,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:54,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:55,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:56,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:56,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:57,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:58,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:45:59,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:00,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:01,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:02,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:03,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:03,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:04,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:06,264][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:46:07,308][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:46:07,310][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:46:08,686][__main__][INFO] - Iteration 877 took 56s (38.90% Gen, 61.09% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 4m 37s. Estimated total time: 15h 40m 15s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 1s, 500 more iterations: 7h 50m 7s. +[2025-08-20 21:46:08,688][__main__][INFO] - Starting iteration 877. +[2025-08-20 21:46:33,099][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:46:33,101][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:46:33,107][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:46:35,537][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:46:35,539][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:46:35,545][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:46:35,547][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:46:35,548][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:46:35,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:36,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:37,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:38,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:39,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:39,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:40,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:41,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:42,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:42,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:43,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:44,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:45,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:46,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:46,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:47,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:48,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:49,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:50,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:50,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:51,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:52,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:53,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:54,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:54,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:55,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:56,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:57,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:58,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:46:59,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:00,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:00,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:02,560][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:47:03,484][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:47:03,486][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:47:04,860][__main__][INFO] - Iteration 878 took 56s (39.12% Gen, 60.88% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 59m 37s. Estimated total time: 15h 36m 11s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 37s, 500 more iterations: 7h 48m 5s. +[2025-08-20 21:47:04,861][__main__][INFO] - Starting iteration 878. +[2025-08-20 21:47:29,303][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:47:29,304][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:47:29,311][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:47:31,750][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:47:31,751][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:47:31,758][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:47:31,760][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:47:31,760][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:47:32,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:32,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:33,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:34,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:35,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:36,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:36,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:37,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:38,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:39,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:39,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:40,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:41,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:42,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:43,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:43,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:44,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:45,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:46,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:47,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:47,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:49,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:49,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:50,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:51,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:52,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:53,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:53,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:54,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:55,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:56,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:57,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:47:58,773][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:47:59,838][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:47:59,840][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:48:01,293][__main__][INFO] - Iteration 879 took 56s (38.97% Gen, 61.03% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 3m 0s. Estimated total time: 15h 40m 31s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 3s, 500 more iterations: 7h 50m 15s. +[2025-08-20 21:48:01,294][__main__][INFO] - Starting iteration 879. +[2025-08-20 21:48:25,651][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:48:25,652][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:48:25,659][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:48:28,117][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:48:28,119][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:48:28,125][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:48:28,127][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:48:28,128][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:48:28,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:29,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:30,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:30,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:31,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:32,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:33,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:33,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:34,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:35,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:36,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:37,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:37,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:38,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:39,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:40,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:41,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:41,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:42,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:43,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:44,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:45,092][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:45,887][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:46,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:47,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:48,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:49,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:49,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:51,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:51,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:52,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:53,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:48:55,206][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:48:56,145][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:48:56,147][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:48:57,454][__main__][INFO] - Iteration 880 took 56s (39.02% Gen, 60.98% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 57m 32s. Estimated total time: 15h 35m 59s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 35s, 500 more iterations: 7h 47m 59s. +[2025-08-20 21:48:57,455][__main__][INFO] - Starting iteration 880. +[2025-08-20 21:49:22,225][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:49:22,226][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:49:22,233][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:49:24,706][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:49:24,707][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:49:24,714][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:49:24,716][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:49:24,716][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:49:25,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:25,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:26,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:27,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:28,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:28,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:29,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:30,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:31,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:32,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:32,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:33,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:34,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:35,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:36,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:36,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:37,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:38,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:39,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:40,065][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:40,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:42,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:42,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:43,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:44,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:45,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:46,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:46,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:47,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:48,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:49,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:50,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:49:51,657][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:49:52,616][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:49:52,617][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:49:54,003][__main__][INFO] - Iteration 881 took 56s (39.41% Gen, 60.59% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 3m 4s. Estimated total time: 15h 42m 26s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 14s, 500 more iterations: 7h 51m 13s. +[2025-08-20 21:49:54,004][__main__][INFO] - Starting iteration 881. +[2025-08-20 21:50:18,419][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:50:18,421][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:50:18,427][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:50:20,868][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:50:20,869][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:50:20,876][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:50:20,878][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:50:20,878][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:50:21,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:21,968][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:22,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:23,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:24,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:25,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:25,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:26,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:27,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:28,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:29,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:29,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:30,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:31,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:32,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:33,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:33,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:34,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:35,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:36,257][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:37,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:37,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:38,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:39,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:40,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:41,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:42,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:43,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:43,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:44,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:45,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:46,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:50:47,983][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:50:48,948][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:50:48,949][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:50:50,367][__main__][INFO] - Iteration 882 took 56s (39.01% Gen, 60.99% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 59m 2s. Estimated total time: 15h 39m 21s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 56s, 500 more iterations: 7h 49m 40s. +[2025-08-20 21:50:50,368][__main__][INFO] - Starting iteration 882. +[2025-08-20 21:51:14,765][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:51:14,776][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:51:14,783][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:51:17,275][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:51:17,276][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:51:17,283][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:51:17,285][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:51:17,285][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:51:17,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:18,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:19,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:19,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:20,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:21,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:22,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:23,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:23,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:24,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:25,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:26,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:27,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:27,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:28,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:29,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:30,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:31,091][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:31,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:32,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:33,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:34,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:35,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:35,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:36,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:37,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:38,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:39,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:39,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:41,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:41,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:42,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:51:44,399][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:51:45,560][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:51:45,562][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:51:46,952][__main__][INFO] - Iteration 883 took 56s (38.77% Gen, 61.23% Train). Generation: 21s, Training: 34s. Estimated remaining time: 2h 1m 48s. Estimated total time: 15h 43m 3s. Time estimates for 10 more iterations: 9m 25s, 100 more iterations: 1h 34m 18s, 500 more iterations: 7h 51m 31s. +[2025-08-20 21:51:46,954][__main__][INFO] - Starting iteration 883. +[2025-08-20 21:52:11,328][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:52:11,329][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:52:11,335][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:52:13,791][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:52:13,793][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:52:13,799][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:52:13,801][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:52:13,802][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:52:14,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:14,890][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:15,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:16,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:17,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:18,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:18,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:19,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:20,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:21,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:22,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:22,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:23,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:24,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:25,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:26,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:26,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:27,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:28,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:29,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:29,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:30,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:31,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:32,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:33,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:33,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:35,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:36,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:36,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:37,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:38,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:39,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:52:40,808][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:52:41,780][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:52:41,781][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:52:43,183][__main__][INFO] - Iteration 884 took 56s (39.00% Gen, 61.00% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 54m 56s. Estimated total time: 15h 37m 8s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 42s, 500 more iterations: 7h 48m 34s. +[2025-08-20 21:52:43,185][__main__][INFO] - Starting iteration 884. +[2025-08-20 21:53:07,448][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:53:07,450][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:53:07,456][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:53:09,913][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:53:09,915][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:53:09,921][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:53:09,923][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:53:09,924][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:53:10,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:11,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:11,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:12,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:13,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:14,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:14,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:15,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:16,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:17,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:18,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:18,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:19,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:20,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:21,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:22,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:22,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:23,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:24,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:25,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:26,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:26,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:28,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:29,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:29,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:30,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:31,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:32,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:32,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:33,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:34,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:35,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:53:37,039][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:53:37,975][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:53:37,977][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:53:39,374][__main__][INFO] - Iteration 885 took 56s (38.82% Gen, 61.18% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 53m 21s. Estimated total time: 15h 36m 29s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 38s, 500 more iterations: 7h 48m 14s. +[2025-08-20 21:53:39,376][__main__][INFO] - Starting iteration 885. +[2025-08-20 21:54:04,108][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:54:04,109][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:54:04,115][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:54:06,571][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:54:06,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:54:06,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:54:06,580][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:54:06,581][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:54:06,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:07,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:08,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:09,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:10,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:10,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:11,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:12,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:13,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:14,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:14,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:15,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:16,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:17,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:18,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:18,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:19,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:20,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:21,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:21,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:22,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:23,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:24,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:25,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:25,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:26,760][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:27,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:28,351][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:29,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:29,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:30,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:32,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:54:33,735][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:54:34,693][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:54:34,695][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:54:36,054][__main__][INFO] - Iteration 886 took 56s (39.31% Gen, 60.69% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 0m 32s. Estimated total time: 15h 44m 37s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 27s, 500 more iterations: 7h 52m 18s. +[2025-08-20 21:54:36,055][__main__][INFO] - Starting iteration 886. +[2025-08-20 21:55:00,475][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:00,476][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:00,483][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:02,936][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:02,937][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:02,944][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:02,946][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:55:02,946][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:55:03,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:04,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:04,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:05,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:06,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:07,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:08,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:08,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:09,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:10,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:11,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:11,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:12,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:13,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:14,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:15,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:15,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:16,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:17,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:18,347][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:19,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:19,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:20,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:21,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:22,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:23,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:24,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:25,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:25,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:26,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:27,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:28,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:55:29,967][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:55:31,015][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:55:31,018][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:55:32,454][__main__][INFO] - Iteration 887 took 56s (38.95% Gen, 61.05% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 54m 57s. Estimated total time: 15h 39m 58s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 59s, 500 more iterations: 7h 49m 59s. +[2025-08-20 21:55:32,456][__main__][INFO] - Starting iteration 887. +[2025-08-20 21:55:56,877][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:56,878][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:56,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:59,342][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:59,343][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:59,350][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:55:59,352][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:55:59,353][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:55:59,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:00,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:01,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:02,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:02,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:03,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:04,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:05,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:06,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:06,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:07,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:08,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:09,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:09,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:10,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:11,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:12,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:13,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:13,971][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:14,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:15,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:16,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:17,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:17,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:18,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:19,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:20,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:21,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:22,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:23,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:24,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:24,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:26,410][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:56:27,353][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:56:27,355][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:56:28,766][__main__][INFO] - Iteration 888 took 56s (39.02% Gen, 60.98% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 52m 32s. Estimated total time: 15h 38m 29s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 50s, 500 more iterations: 7h 49m 14s. +[2025-08-20 21:56:28,768][__main__][INFO] - Starting iteration 888. +[2025-08-20 21:56:52,933][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:56:52,934][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:56:52,941][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:56:55,395][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:56:55,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:56:55,403][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:56:55,405][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:56:55,405][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:56:55,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:56,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:57,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:58,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:58,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:56:59,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:00,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:01,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:02,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:02,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:03,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:04,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:05,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:06,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:06,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:07,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:08,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:09,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:10,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:10,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:12,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:12,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:13,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:14,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:15,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:16,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:16,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:17,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:18,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:19,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:20,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:20,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:22,502][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:57:23,491][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:57:23,493][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:57:24,820][__main__][INFO] - Iteration 889 took 56s (38.76% Gen, 61.23% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 47m 18s. Estimated total time: 15h 34m 12s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 25s, 500 more iterations: 7h 47m 6s. +[2025-08-20 21:57:24,822][__main__][INFO] - Starting iteration 889. +[2025-08-20 21:57:49,181][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:57:49,183][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:57:49,189][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:57:51,645][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:57:51,646][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:57:51,653][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:57:51,655][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:57:51,656][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:57:51,960][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:52,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:53,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:54,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:55,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:55,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:56,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:57,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:58,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:59,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:57:59,907][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:00,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:01,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:02,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:03,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:03,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:04,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:05,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:06,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:07,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:07,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:08,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:09,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:10,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:11,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:12,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:13,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:13,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:14,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:15,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:16,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:17,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:18,744][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:58:19,688][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:58:19,690][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:58:21,178][__main__][INFO] - Iteration 890 took 56s (38.89% Gen, 61.11% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 51m 25s. Estimated total time: 15h 39m 15s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 55s, 500 more iterations: 7h 49m 37s. +[2025-08-20 21:58:21,180][__main__][INFO] - Starting iteration 890. +[2025-08-20 21:58:45,819][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:58:45,820][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:58:45,826][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:58:48,310][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:58:48,311][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:58:48,318][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:58:48,320][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:58:48,320][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:58:48,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:49,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:50,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:51,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:51,795][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:52,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:53,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:54,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:54,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:55,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:56,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:57,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:58,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:58,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:58:59,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:00,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:01,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:02,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:02,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:03,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:04,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:05,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:06,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:06,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:07,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:08,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:09,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:10,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:10,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:11,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:13,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:13,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:15,379][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 21:59:16,440][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 21:59:16,443][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 21:59:17,827][__main__][INFO] - Iteration 891 took 56s (39.14% Gen, 60.86% Train). Generation: 22s, Training: 34s. Estimated remaining time: 1h 55m 20s. Estimated total time: 15h 44m 7s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 24s, 500 more iterations: 7h 52m 3s. +[2025-08-20 21:59:17,830][__main__][INFO] - Starting iteration 891. +[2025-08-20 21:59:42,181][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:59:42,183][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:59:42,189][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:59:44,625][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:59:44,627][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:59:44,633][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 21:59:44,635][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 21:59:44,636][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 21:59:44,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:45,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:46,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:47,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:48,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:48,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:49,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:50,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:51,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:52,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:52,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:53,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:54,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:55,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:56,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:56,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:57,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:58,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 21:59:59,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:00,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:00,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:01,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:02,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:03,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:04,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:04,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:05,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:06,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:07,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:08,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:09,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:10,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:11,691][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:00:12,667][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:00:12,669][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:00:14,490][__main__][INFO] - Iteration 892 took 56s (38.67% Gen, 61.32% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 54m 36s. Estimated total time: 15h 44m 19s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 25s, 500 more iterations: 7h 52m 9s. +[2025-08-20 22:00:14,492][__main__][INFO] - Starting iteration 892. +[2025-08-20 22:00:38,789][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:00:38,790][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:00:38,797][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:00:41,258][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:00:41,259][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:00:41,265][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:00:41,268][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:00:41,268][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:00:41,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:42,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:43,156][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:43,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:44,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:45,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:46,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:47,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:47,929][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:48,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:49,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:50,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:51,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:51,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:52,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:53,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:54,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:55,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:55,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:56,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:57,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:58,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:00:59,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:00,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:01,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:01,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:02,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:03,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:04,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:05,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:05,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:06,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:08,259][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:01:09,176][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:01:09,177][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:01:10,688][__main__][INFO] - Iteration 893 took 56s (38.87% Gen, 61.13% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 45m 55s. Estimated total time: 15h 36m 35s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 39s, 500 more iterations: 7h 48m 17s. +[2025-08-20 22:01:10,689][__main__][INFO] - Starting iteration 893. +[2025-08-20 22:01:35,106][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:01:35,107][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:01:35,113][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:01:37,571][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:01:37,572][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:01:37,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:01:37,581][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:01:37,581][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:01:37,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:38,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:39,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:40,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:41,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:41,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:42,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:43,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:44,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:45,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:45,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:46,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:47,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:48,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:49,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:49,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:50,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:51,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:52,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:52,989][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:53,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:54,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:55,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:56,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:56,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:57,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:58,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:01:59,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:00,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:01,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:02,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:03,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:04,607][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:02:05,550][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:02:05,551][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:02:06,889][__main__][INFO] - Iteration 894 took 56s (39.09% Gen, 60.91% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 45m 3s. Estimated total time: 15h 36m 39s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 39s, 500 more iterations: 7h 48m 19s. +[2025-08-20 22:02:06,891][__main__][INFO] - Starting iteration 894. +[2025-08-20 22:02:31,231][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:02:31,233][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:02:31,239][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:02:33,691][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:02:33,692][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:02:33,698][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:02:33,701][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:02:33,701][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:02:34,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:34,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:35,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:36,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:37,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:37,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:38,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:39,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:40,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:41,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:41,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:42,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:43,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:44,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:45,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:45,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:46,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:47,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:48,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:49,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:49,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:50,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:51,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:52,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:53,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:53,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:55,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:55,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:56,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:57,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:58,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:02:59,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:00,734][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:03:01,722][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:03:01,723][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:03:03,164][__main__][INFO] - Iteration 895 took 56s (38.91% Gen, 61.08% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 45m 20s. Estimated total time: 15h 37m 52s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 47s, 500 more iterations: 7h 48m 56s. +[2025-08-20 22:03:03,165][__main__][INFO] - Starting iteration 895. +[2025-08-20 22:03:27,809][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:03:27,810][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:03:27,816][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:03:30,315][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:03:30,316][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:03:30,322][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:03:30,325][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:03:30,325][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:03:30,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:31,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:32,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:33,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:33,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:34,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:35,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:36,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:36,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:37,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:38,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:39,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:40,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:40,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:41,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:42,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:43,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:44,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:44,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:45,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:46,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:47,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:48,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:49,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:50,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:50,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:51,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:52,551][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:53,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:54,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:54,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:55,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:03:57,314][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:03:58,355][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:03:58,358][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:03:59,791][__main__][INFO] - Iteration 896 took 56s (39.13% Gen, 60.87% Train). Generation: 22s, Training: 34s. Estimated remaining time: 1h 50m 16s. Estimated total time: 15h 43m 44s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 22s, 500 more iterations: 7h 51m 52s. +[2025-08-20 22:03:59,792][__main__][INFO] - Starting iteration 896. +[2025-08-20 22:04:24,199][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:04:24,200][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:04:24,206][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:04:26,654][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:04:26,655][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:04:26,661][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:04:26,664][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:04:26,664][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:04:26,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:27,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:28,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:29,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:30,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:30,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:31,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:32,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:33,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:34,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:34,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:35,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:36,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:37,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:38,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:38,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:39,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:40,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:41,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:42,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:43,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:43,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:45,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:46,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:47,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:48,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:49,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:50,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:51,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:51,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:52,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:53,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:04:55,052][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:28, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:04:55,989][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:04:55,991][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:04:57,349][__main__][INFO] - Iteration 897 took 57s (38.13% Gen, 61.87% Train). Generation: 21s, Training: 35s. Estimated remaining time: 2h 4m 50s. Estimated total time: 15h 59m 16s. Time estimates for 10 more iterations: 9m 35s, 100 more iterations: 1h 35m 55s, 500 more iterations: 7h 59m 38s. +[2025-08-20 22:04:57,350][__main__][INFO] - Starting iteration 897. +[2025-08-20 22:05:21,616][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:05:21,617][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:05:21,623][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:05:24,068][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:05:24,069][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:05:24,076][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:05:24,079][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:05:24,079][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:05:24,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:25,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:25,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:26,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:27,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:28,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:29,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:29,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:30,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:31,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:32,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:33,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:33,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:34,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:35,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:36,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:37,093][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:37,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:38,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:39,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:40,272][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:41,068][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:41,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:43,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:43,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:44,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:45,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:46,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:47,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:47,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:48,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:49,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:05:51,000][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:05:52,147][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:05:52,149][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:05:53,510][__main__][INFO] - Iteration 898 took 56s (38.85% Gen, 61.15% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 40m 36s. Estimated total time: 15h 35m 59s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 35s, 500 more iterations: 7h 47m 59s. +[2025-08-20 22:05:53,511][__main__][INFO] - Starting iteration 898. +[2025-08-20 22:06:17,945][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:06:17,946][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:06:17,952][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:06:20,425][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:06:20,426][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:06:20,433][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:06:20,435][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:06:20,436][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:06:20,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:21,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:22,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:23,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:23,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:24,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:25,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:26,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:27,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:27,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:28,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:29,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:30,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:31,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:31,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:32,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:33,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:34,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:35,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:35,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:36,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:37,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:38,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:39,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:39,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:40,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:41,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:42,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:42,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:44,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:45,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:45,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:06:47,518][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:06:48,466][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:06:48,468][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:06:49,858][__main__][INFO] - Iteration 899 took 56s (39.01% Gen, 60.99% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 42m 47s. Estimated total time: 15h 39m 6s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 54s, 500 more iterations: 7h 49m 33s. +[2025-08-20 22:06:49,860][__main__][INFO] - Starting iteration 899. +[2025-08-20 22:07:14,221][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:07:14,222][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:07:14,229][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:07:16,701][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:07:16,702][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:07:16,708][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:07:16,711][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:07:16,711][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:07:17,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:17,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:18,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:19,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:20,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:20,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:21,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:22,579][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:23,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:24,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:24,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:25,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:26,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:27,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:28,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:28,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:29,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:30,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:31,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:32,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:32,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:33,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:34,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:35,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:36,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:37,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:38,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:39,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:39,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:40,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:41,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:42,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:07:43,765][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:07:44,812][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:07:44,814][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:07:46,115][__main__][INFO] - Iteration 900 took 56s (38.93% Gen, 61.07% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 40m 19s. Estimated total time: 15h 37m 35s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 45s, 500 more iterations: 7h 48m 47s. +[2025-08-20 22:07:46,117][__main__][INFO] - Starting iteration 900. +[2025-08-20 22:08:10,882][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:08:10,884][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:08:10,890][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:08:13,338][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:08:13,339][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:08:13,346][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:08:13,349][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:08:13,349][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:08:13,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:14,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:15,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:16,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:16,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:17,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:18,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:19,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:19,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:20,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:21,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:22,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:23,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:23,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:24,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:25,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:26,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:27,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:27,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:28,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:29,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:30,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:31,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:32,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:33,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:33,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:34,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:35,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:36,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:37,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:37,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:38,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:08:40,254][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:08:41,953][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:08:41,956][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:08:45,906][__main__][INFO] - Iteration 901 took 59s (37.33% Gen, 58.41% Train). Generation: 22s, Training: 34s. Estimated remaining time: 2h 38m 14s. Estimated total time: 16h 36m 29s. Time estimates for 10 more iterations: 9m 57s, 100 more iterations: 1h 39m 38s, 500 more iterations: 8h 18m 14s. +[2025-08-20 22:08:45,908][__main__][INFO] - Starting iteration 901. +[2025-08-20 22:09:10,329][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:09:10,330][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:09:10,337][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:09:12,770][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:09:12,772][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:09:12,778][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:09:12,780][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:09:12,781][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:09:13,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:13,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:14,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:15,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:16,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:17,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:17,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:18,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:19,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:20,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:21,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:21,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:22,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:23,393][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:24,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:24,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:25,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:26,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:27,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:28,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:28,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:29,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:30,545][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:31,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:32,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:32,932][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:33,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:35,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:35,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:36,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:37,386][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:38,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:09:39,754][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:09:40,702][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:09:40,704][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:09:42,189][__main__][INFO] - Iteration 902 took 56s (39.07% Gen, 60.92% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 38m 49s. Estimated total time: 15h 38m 0s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 48s, 500 more iterations: 7h 49m 0s. +[2025-08-20 22:09:42,190][__main__][INFO] - Starting iteration 902. +[2025-08-20 22:10:06,411][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:10:06,413][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:10:06,419][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:10:08,867][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:10:08,868][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:10:08,874][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:10:08,876][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:10:08,877][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:10:09,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:09,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:10,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:11,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:12,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:13,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:13,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:14,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:15,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:16,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:17,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:17,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:18,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:19,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:20,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:21,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:21,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:22,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:23,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:24,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:25,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:25,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:26,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:27,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:28,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:29,043][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:29,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:31,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:31,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:32,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:33,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:34,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:10:35,956][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:10:37,261][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:10:37,263][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:10:38,525][__main__][INFO] - Iteration 903 took 56s (38.68% Gen, 61.32% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 38m 46s. Estimated total time: 15h 38m 54s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 53s, 500 more iterations: 7h 49m 27s. +[2025-08-20 22:10:38,526][__main__][INFO] - Starting iteration 903. +[2025-08-20 22:11:02,894][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:02,896][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:02,902][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:05,354][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:05,355][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:05,361][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:05,363][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:11:05,364][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:11:05,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:06,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:07,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:08,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:08,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:09,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:10,422][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:11,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:12,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:12,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:13,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:14,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:15,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:15,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:16,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:17,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:18,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:19,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:19,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:20,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:21,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:22,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:23,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:23,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:24,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:25,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:26,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:27,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:27,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:29,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:29,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:30,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:11:32,382][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:11:33,310][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:11:33,312][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:11:34,706][__main__][INFO] - Iteration 904 took 56s (38.99% Gen, 61.00% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 35m 15s. Estimated total time: 15h 36m 19s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 37s, 500 more iterations: 7h 48m 9s. +[2025-08-20 22:11:34,707][__main__][INFO] - Starting iteration 904. +[2025-08-20 22:11:59,067][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:59,068][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:11:59,074][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:01,514][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:01,515][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:01,521][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:01,524][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:12:01,524][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:12:01,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:02,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:03,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:04,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:04,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:05,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:06,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:07,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:08,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:08,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:09,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:10,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:11,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:12,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:12,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:13,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:14,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:15,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:16,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:16,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:17,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:18,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:19,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:20,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:21,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:22,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:22,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:23,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:24,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:25,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:26,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:26,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:28,454][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:12:29,606][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:12:29,608][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:12:31,064][__main__][INFO] - Iteration 905 took 56s (38.90% Gen, 61.10% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 37m 16s. Estimated total time: 15h 39m 16s. Time estimates for 10 more iterations: 9m 23s, 100 more iterations: 1h 33m 55s, 500 more iterations: 7h 49m 38s. +[2025-08-20 22:12:31,066][__main__][INFO] - Starting iteration 905. +[2025-08-20 22:12:56,276][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:56,278][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:56,284][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:58,741][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:58,742][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:58,749][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:12:58,751][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:12:58,751][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:12:59,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:12:59,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:00,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:01,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:02,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:03,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:03,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:04,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:05,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:06,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:06,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:07,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:08,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:09,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:10,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:10,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:11,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:12,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:13,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:14,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:14,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:15,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:16,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:17,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:18,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:18,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:20,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:21,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:21,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:22,621][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:23,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:24,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:25,821][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:13:26,798][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:13:26,800][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:13:28,116][__main__][INFO] - Iteration 906 took 57s (39.86% Gen, 60.14% Train). Generation: 22s, Training: 34s. Estimated remaining time: 1h 47m 52s. Estimated total time: 15h 50m 49s. Time estimates for 10 more iterations: 9m 30s, 100 more iterations: 1h 35m 4s, 500 more iterations: 7h 55m 24s. +[2025-08-20 22:13:28,117][__main__][INFO] - Starting iteration 906. +[2025-08-20 22:13:52,858][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:13:52,859][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:13:52,865][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:13:55,326][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:13:55,327][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:13:55,334][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:13:55,336][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:13:55,336][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:13:55,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:56,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:57,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:58,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:58,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:13:59,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:00,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:01,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:01,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:02,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:03,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:04,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:05,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:05,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:06,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:07,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:08,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:09,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:09,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:10,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:11,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:12,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:13,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:13,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:14,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:15,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:16,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:17,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:18,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:19,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:19,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:20,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:22,265][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:14:23,267][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:14:23,269][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:14:24,764][__main__][INFO] - Iteration 907 took 56s (39.35% Gen, 60.65% Train). Generation: 22s, Training: 34s. Estimated remaining time: 1h 40m 12s. Estimated total time: 15h 44m 6s. Time estimates for 10 more iterations: 9m 26s, 100 more iterations: 1h 34m 24s, 500 more iterations: 7h 52m 3s. +[2025-08-20 22:14:24,766][__main__][INFO] - Starting iteration 907. +[2025-08-20 22:14:49,073][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:14:49,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:14:49,081][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:14:51,516][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:14:51,518][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:14:51,524][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:14:51,526][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:14:51,527][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:14:51,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:52,615][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:53,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:54,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:54,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:55,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:56,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:57,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:58,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:58,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:14:59,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:00,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:01,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:02,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:02,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:03,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:04,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:05,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:06,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:06,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:07,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:08,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:09,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:10,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:10,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:11,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:12,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:13,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:14,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:14,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:15,602][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:16,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:18,078][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:15:19,430][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:15:19,432][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:15:20,831][__main__][INFO] - Iteration 908 took 56s (39.00% Gen, 61.00% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 29m 35s. Estimated total time: 15h 34m 24s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 26s, 500 more iterations: 7h 47m 12s. +[2025-08-20 22:15:20,832][__main__][INFO] - Starting iteration 908. +[2025-08-20 22:15:45,166][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:15:45,167][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:15:45,174][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:15:47,678][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:15:47,679][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:15:47,685][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:15:47,688][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:15:47,688][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:15:47,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:48,780][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:49,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:50,363][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:51,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:51,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:52,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:53,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:54,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:55,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:55,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:56,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:57,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:58,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:59,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:15:59,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:00,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:01,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:02,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:03,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:03,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:04,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:05,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:06,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:07,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:07,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:08,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:09,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:10,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:11,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:12,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:13,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:14,889][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:16:15,867][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:16:15,869][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:16:17,264][__main__][INFO] - Iteration 909 took 56s (38.71% Gen, 61.29% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 34m 44s. Estimated total time: 15h 40m 30s. Time estimates for 10 more iterations: 9m 24s, 100 more iterations: 1h 34m 3s, 500 more iterations: 7h 50m 15s. +[2025-08-20 22:16:17,265][__main__][INFO] - Starting iteration 909. +[2025-08-20 22:16:41,475][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:16:41,477][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:16:41,483][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:16:43,935][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:16:43,937][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:16:43,943][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:16:43,945][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:16:43,946][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:16:44,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:45,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:45,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:46,623][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:47,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:48,209][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:49,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:49,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:50,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:51,384][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:52,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:52,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:53,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:54,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:55,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:56,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:56,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:57,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:58,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:16:59,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:00,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:00,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:01,722][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:02,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:03,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:04,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:05,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:06,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:06,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:07,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:08,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:09,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:10,961][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:17:11,921][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:17:11,922][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:17:13,308][__main__][INFO] - Iteration 910 took 56s (38.83% Gen, 61.16% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 27m 20s. Estimated total time: 15h 34m 2s. Time estimates for 10 more iterations: 9m 20s, 100 more iterations: 1h 33m 24s, 500 more iterations: 7h 47m 1s. +[2025-08-20 22:17:13,310][__main__][INFO] - Starting iteration 910. +[2025-08-20 22:17:37,442][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:17:37,444][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:17:37,450][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:17:39,917][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:17:39,918][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:17:39,925][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:17:39,927][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:17:39,928][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:17:40,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:41,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:41,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:42,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:43,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:44,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:44,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:45,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:46,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:47,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:48,165][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:48,959][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:49,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:50,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:51,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:52,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:52,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:53,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:54,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:55,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:56,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:57,374][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:58,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:58,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:17:59,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:00,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:01,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:02,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:02,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:03,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:04,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:05,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:06,961][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:18:07,921][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:18:07,922][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:18:09,268][__main__][INFO] - Iteration 911 took 55s (38.74% Gen, 61.25% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 24m 59s. Estimated total time: 15h 32m 37s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 15s, 500 more iterations: 7h 46m 18s. +[2025-08-20 22:18:09,269][__main__][INFO] - Starting iteration 911. +[2025-08-20 22:18:33,556][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:18:33,557][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:18:33,564][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:18:36,024][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:18:36,025][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:18:36,032][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:18:36,034][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:18:36,034][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:18:36,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:37,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:37,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:38,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:39,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:40,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:41,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:41,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:42,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:43,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:44,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:45,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:45,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:46,649][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:47,444][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:48,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:49,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:49,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:50,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:51,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:52,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:53,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:53,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:54,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:56,019][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:56,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:57,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:58,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:18:59,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:00,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:00,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:01,591][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:03,221][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:19:04,173][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:19:04,175][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:19:05,493][__main__][INFO] - Iteration 912 took 56s (38.82% Gen, 61.18% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 28m 29s. Estimated total time: 15h 37m 3s. Time estimates for 10 more iterations: 9m 22s, 100 more iterations: 1h 33m 42s, 500 more iterations: 7h 48m 31s. +[2025-08-20 22:19:05,495][__main__][INFO] - Starting iteration 912. +[2025-08-20 22:19:29,594][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:19:29,596][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:19:29,602][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:19:32,047][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:19:32,048][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:19:32,054][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:19:32,057][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:19:32,057][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:19:32,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:33,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:33,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:34,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:35,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:36,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:37,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:37,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:38,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:39,497][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:40,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:41,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:41,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:42,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:43,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:44,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:45,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:45,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:46,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:47,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:48,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:49,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:49,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:50,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:51,425][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:52,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:53,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:54,271][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:55,067][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:55,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:56,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:57,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:19:59,132][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:20:00,143][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:20:00,146][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:20:01,637][__main__][INFO] - Iteration 913 took 56s (38.57% Gen, 61.43% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 26m 11s. Estimated total time: 15h 35m 42s. Time estimates for 10 more iterations: 9m 21s, 100 more iterations: 1h 33m 34s, 500 more iterations: 7h 47m 51s. +[2025-08-20 22:20:01,638][__main__][INFO] - Starting iteration 913. +[2025-08-20 22:20:24,975][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:20:24,976][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:20:24,982][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:20:27,430][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:20:27,432][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:20:27,438][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:20:27,440][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:20:27,441][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:20:27,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:28,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:29,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:30,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:30,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:31,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:32,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:33,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:34,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:34,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:35,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:36,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:37,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:38,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:38,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:39,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:40,432][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:41,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:42,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:42,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:44,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:44,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:45,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:46,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:47,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:48,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:48,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:49,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:50,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:51,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:52,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:52,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:20:54,487][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:20:55,498][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:20:55,501][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:20:57,068][__main__][INFO] - Iteration 914 took 55s (37.69% Gen, 62.31% Train). Generation: 20s, Training: 34s. Estimated remaining time: 1h 13m 23s. Estimated total time: 15h 23m 49s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 22s, 500 more iterations: 7h 41m 54s. +[2025-08-20 22:20:57,070][__main__][INFO] - Starting iteration 914. +[2025-08-20 22:21:21,086][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:21:21,087][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:21:21,093][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:21:23,560][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:21:23,561][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:21:23,567][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:21:23,570][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:21:23,570][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:21:23,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:24,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:25,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:26,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:27,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:27,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:28,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:29,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:30,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:31,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:31,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:32,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:33,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:34,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:34,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:35,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:36,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:37,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:38,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:38,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:39,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:40,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:41,343][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:42,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:42,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:43,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:45,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:45,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:46,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:47,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:48,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:49,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:21:50,682][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:21:51,626][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:21:51,627][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:21:52,970][__main__][INFO] - Iteration 915 took 55s (38.57% Gen, 61.42% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 20m 17s. Estimated total time: 15h 31m 39s. Time estimates for 10 more iterations: 9m 18s, 100 more iterations: 1h 33m 9s, 500 more iterations: 7h 45m 49s. +[2025-08-20 22:21:52,971][__main__][INFO] - Starting iteration 915. +[2025-08-20 22:22:16,549][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:22:16,550][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:22:16,556][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:22:18,998][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:22:19,000][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:22:19,007][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:22:19,009][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:22:19,010][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:22:19,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:20,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:20,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:21,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:22,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:23,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:24,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:24,857][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:25,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:26,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:27,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:28,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:28,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:29,619][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:30,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:31,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:32,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:32,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:34,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:34,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:35,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:36,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:37,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:38,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:38,804][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:39,599][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:40,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:41,194][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:41,991][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:42,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:43,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:44,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:22:46,036][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:22:46,999][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:22:47,000][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:22:48,515][__main__][INFO] - Iteration 916 took 55s (38.06% Gen, 61.94% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 13m 26s. Estimated total time: 15h 25m 43s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 34s, 500 more iterations: 7h 42m 51s. +[2025-08-20 22:22:48,517][__main__][INFO] - Starting iteration 916. +[2025-08-20 22:23:12,583][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:23:12,584][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:23:12,591][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:23:15,058][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:23:15,059][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:23:15,066][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:23:15,068][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:23:15,069][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:23:15,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:16,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:16,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:17,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:18,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:19,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:20,124][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:20,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:21,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:22,504][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:23,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:24,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:24,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:25,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:26,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:27,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:28,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:28,860][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:29,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:30,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:31,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:32,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:33,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:34,099][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:34,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:35,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:36,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:37,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:38,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:38,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:39,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:40,468][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:23:42,097][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:23:43,056][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:23:43,057][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:23:44,433][__main__][INFO] - Iteration 917 took 55s (38.61% Gen, 61.39% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 18m 42s. Estimated total time: 15h 31m 55s. Time estimates for 10 more iterations: 9m 19s, 100 more iterations: 1h 33m 11s, 500 more iterations: 7h 45m 57s. +[2025-08-20 22:23:44,434][__main__][INFO] - Starting iteration 917. +[2025-08-20 22:24:08,112][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:24:08,113][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:24:08,119][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:24:10,564][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:24:10,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:24:10,571][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:24:10,574][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:24:10,574][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:24:10,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:11,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:12,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:13,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:14,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:14,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:15,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:16,420][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:17,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:18,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:18,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:19,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:20,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:21,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:21,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:22,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:23,565][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:24,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:25,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:25,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:27,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:28,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:28,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:29,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:30,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:31,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:32,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:32,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:33,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:34,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:35,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:36,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:24:37,702][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:24:38,678][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:24:38,680][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:24:40,219][__main__][INFO] - Iteration 918 took 55s (38.06% Gen, 61.94% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 15m 34s. Estimated total time: 15h 29m 44s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 58s, 500 more iterations: 7h 44m 52s. +[2025-08-20 22:24:40,220][__main__][INFO] - Starting iteration 918. +[2025-08-20 22:25:02,954][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:02,956][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:02,962][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:05,432][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:05,433][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:05,439][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:05,442][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:25:05,442][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:25:05,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:06,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:07,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:08,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:08,905][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:09,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:10,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:11,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:12,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:12,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:13,657][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:14,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:15,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:16,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:16,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:17,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:18,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:19,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:20,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:21,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:22,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:22,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:23,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:24,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:25,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:26,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:26,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:27,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:28,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:29,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:30,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:30,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:25:32,576][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:25:33,508][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:25:33,510][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:25:34,824][__main__][INFO] - Iteration 919 took 54s (37.14% Gen, 62.85% Train). Generation: 20s, Training: 34s. Estimated remaining time: 54m 59s. Estimated total time: 15h 10m 3s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 0s, 500 more iterations: 7h 35m 1s. +[2025-08-20 22:25:34,826][__main__][INFO] - Starting iteration 919. +[2025-08-20 22:25:58,053][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:58,055][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:25:58,061][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:00,519][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:00,521][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:00,527][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:00,529][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:26:00,530][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:26:00,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:01,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:02,410][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:03,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:03,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:04,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:05,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:06,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:07,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:07,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:08,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:09,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:10,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:11,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:11,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:12,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:13,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:14,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:15,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:16,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:17,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:17,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:18,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:19,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:20,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:21,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:21,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:22,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:23,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:24,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:25,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:25,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:27,558][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:26:28,515][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:26:28,516][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:26:29,883][__main__][INFO] - Iteration 920 took 55s (37.74% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 1h 1m 38s. Estimated total time: 15h 17m 36s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 45s, 500 more iterations: 7h 38m 48s. +[2025-08-20 22:26:29,884][__main__][INFO] - Starting iteration 920. +[2025-08-20 22:26:53,036][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:53,037][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:53,043][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:55,484][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:55,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:55,491][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:26:55,494][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:26:55,494][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:26:55,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:56,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:57,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:58,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:58,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:26:59,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:00,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:01,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:02,116][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:02,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:03,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:04,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:05,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:06,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:06,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:07,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:08,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:09,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:10,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:10,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:11,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:12,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:13,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:14,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:14,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:16,184][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:16,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:17,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:18,571][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:19,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:20,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:20,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:22,576][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:27:23,540][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:27:23,542][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:27:24,816][__main__][INFO] - Iteration 921 took 54s (37.72% Gen, 62.28% Train). Generation: 20s, Training: 34s. Estimated remaining time: 58m 37s. Estimated total time: 15h 15m 31s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 33s, 500 more iterations: 7h 37m 45s. +[2025-08-20 22:27:24,818][__main__][INFO] - Starting iteration 921. +[2025-08-20 22:27:47,953][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:27:47,954][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:27:47,960][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:27:50,414][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:27:50,415][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:27:50,422][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:27:50,424][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:27:50,424][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:27:50,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:51,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:52,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:53,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:53,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:54,683][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:55,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:56,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:57,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:57,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:58,652][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:27:59,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:00,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:01,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:01,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:02,624][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:03,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:04,214][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:05,013][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:05,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:07,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:07,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:08,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:09,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:10,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:11,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:11,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:12,689][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:13,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:14,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:15,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:15,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:17,524][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:28:18,453][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:28:18,455][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:28:19,796][__main__][INFO] - Iteration 922 took 54s (37.63% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 58m 29s. Estimated total time: 15h 16m 18s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 37s, 500 more iterations: 7h 38m 9s. +[2025-08-20 22:28:19,798][__main__][INFO] - Starting iteration 922. +[2025-08-20 22:28:42,672][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:28:42,673][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:28:42,679][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:28:45,134][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:28:45,135][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:28:45,142][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:28:45,144][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:28:45,145][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:28:45,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:46,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:47,026][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:47,819][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:48,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:49,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:50,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:50,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:51,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:52,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:53,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:54,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:54,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:55,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:56,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:57,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:58,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:28:59,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:00,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:01,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:01,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:02,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:03,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:04,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:04,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:05,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:06,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:07,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:08,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:08,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:09,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:10,555][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:12,180][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:29:13,142][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:29:13,144][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:29:14,503][__main__][INFO] - Iteration 923 took 54s (37.34% Gen, 62.66% Train). Generation: 20s, Training: 34s. Estimated remaining time: 53m 1s. Estimated total time: 15h 11m 45s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 10s, 500 more iterations: 7h 35m 52s. +[2025-08-20 22:29:14,505][__main__][INFO] - Starting iteration 923. +[2025-08-20 22:29:37,570][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:29:37,571][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:29:37,578][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:29:40,048][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:29:40,050][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:29:40,057][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:29:40,059][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:29:40,059][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:29:40,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:41,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:41,938][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:42,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:43,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:44,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:45,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:45,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:46,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:47,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:48,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:49,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:49,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:50,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:51,454][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:52,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:53,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:53,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:54,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:55,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:56,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:57,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:58,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:29:59,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:00,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:00,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:01,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:02,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:03,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:03,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:04,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:05,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:07,202][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:30:08,126][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:30:08,128][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:30:09,516][__main__][INFO] - Iteration 924 took 55s (37.44% Gen, 62.56% Train). Generation: 20s, Training: 34s. Estimated remaining time: 57m 12s. Estimated total time: 15h 16m 51s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 41s, 500 more iterations: 7h 38m 25s. +[2025-08-20 22:30:09,518][__main__][INFO] - Starting iteration 924. +[2025-08-20 22:30:32,609][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:30:32,610][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:30:32,617][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:30:35,070][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:30:35,072][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:30:35,078][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:30:35,080][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:30:35,081][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:30:35,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:36,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:36,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:37,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:38,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:39,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:40,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:40,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:41,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:42,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:43,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:44,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:44,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:45,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:46,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:47,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:48,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:48,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:49,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:50,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:51,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:52,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:53,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:54,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:54,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:55,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:56,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:57,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:58,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:58,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:30:59,748][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:00,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:02,201][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:31:03,133][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:31:03,134][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:31:04,486][__main__][INFO] - Iteration 925 took 54s (37.53% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 55m 33s. Estimated total time: 15h 16m 7s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 36s, 500 more iterations: 7h 38m 3s. +[2025-08-20 22:31:04,487][__main__][INFO] - Starting iteration 925. +[2025-08-20 22:31:27,384][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:31:27,385][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:31:27,391][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:31:29,844][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:31:29,846][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:31:29,852][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:31:29,854][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:31:29,855][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:31:30,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:30,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:31,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:32,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:33,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:34,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:34,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:35,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:36,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:37,290][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:38,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:38,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:39,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:40,469][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:41,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:42,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:42,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:43,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:44,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:45,755][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:46,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:47,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:48,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:48,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:49,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:50,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:51,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:52,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:52,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:53,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:54,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:55,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:31:56,942][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:31:57,919][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:31:57,921][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:31:59,389][__main__][INFO] - Iteration 926 took 54s (37.25% Gen, 62.74% Train). Generation: 20s, Training: 34s. Estimated remaining time: 53m 32s. Estimated total time: 15h 15m 1s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 30s. +[2025-08-20 22:31:59,390][__main__][INFO] - Starting iteration 926. +[2025-08-20 22:32:22,407][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:32:22,408][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:32:22,415][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:32:24,885][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:32:24,886][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:32:24,893][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:32:24,895][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:32:24,895][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:32:25,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:25,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:26,773][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:27,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:28,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:29,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:29,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:30,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:31,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:32,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:33,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:33,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:34,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:35,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:36,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:37,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:37,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:38,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:39,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:40,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:41,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:41,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:42,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:43,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:44,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:45,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:46,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:47,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:47,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:48,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:49,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:50,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:32:51,954][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:32:52,901][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:32:52,902][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:32:54,197][__main__][INFO] - Iteration 927 took 54s (37.51% Gen, 62.49% Train). Generation: 20s, Training: 34s. Estimated remaining time: 51m 3s. Estimated total time: 15h 13m 26s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 43s. +[2025-08-20 22:32:54,199][__main__][INFO] - Starting iteration 927. +[2025-08-20 22:33:17,773][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:33:17,774][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:33:17,780][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:33:20,240][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:33:20,241][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:33:20,247][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:33:20,250][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:33:20,250][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:33:20,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:21,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:22,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:22,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:23,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:24,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:25,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:26,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:26,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:27,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:28,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:29,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:30,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:30,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:31,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:32,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:33,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:34,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:34,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:35,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:36,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:37,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:38,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:38,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:39,628][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:40,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:41,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:42,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:43,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:44,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:44,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:45,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:33:47,287][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:33:48,262][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:33:48,264][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:33:49,601][__main__][INFO] - Iteration 928 took 55s (38.13% Gen, 61.87% Train). Generation: 21s, Training: 34s. Estimated remaining time: 1h 0m 3s. Estimated total time: 15h 23m 22s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 20s, 500 more iterations: 7h 41m 41s. +[2025-08-20 22:33:49,603][__main__][INFO] - Starting iteration 928. +[2025-08-20 22:34:12,318][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:34:12,320][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:34:12,326][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:34:14,776][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:34:14,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:34:14,784][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:34:14,786][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:34:14,787][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:34:15,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:15,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:16,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:17,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:18,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:19,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:19,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:20,684][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:21,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:22,275][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:23,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:23,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:24,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:25,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:26,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:27,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:27,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:28,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:29,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:30,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:31,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:32,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:33,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:33,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:34,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:35,507][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:36,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:37,094][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:37,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:38,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:39,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:40,277][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:34:41,881][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:34:42,765][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:34:42,766][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:34:44,131][__main__][INFO] - Iteration 929 took 54s (37.16% Gen, 62.84% Train). Generation: 20s, Training: 34s. Estimated remaining time: 44m 34s. Estimated total time: 15h 8m 47s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 52s, 500 more iterations: 7h 34m 23s. +[2025-08-20 22:34:44,132][__main__][INFO] - Starting iteration 929. +[2025-08-20 22:35:07,037][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:35:07,038][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:35:07,044][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:35:09,496][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:35:09,497][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:35:09,504][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:35:09,506][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:35:09,507][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:35:09,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:10,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:11,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:12,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:12,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:13,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:14,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:15,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:16,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:16,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:17,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:18,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:19,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:20,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:20,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:21,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:22,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:23,847][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:24,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:25,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:26,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:27,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:27,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:28,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:29,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:30,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:31,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:31,799][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:32,596][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:33,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:34,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:34,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:35:36,617][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:35:37,619][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:35:37,621][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:35:38,831][__main__][INFO] - Iteration 930 took 54s (37.36% Gen, 62.64% Train). Generation: 20s, Training: 34s. Estimated remaining time: 46m 30s. Estimated total time: 15h 11m 37s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 9s, 500 more iterations: 7h 35m 48s. +[2025-08-20 22:35:38,832][__main__][INFO] - Starting iteration 930. +[2025-08-20 22:36:01,984][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:01,985][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:01,991][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:04,433][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:04,434][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:04,441][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:04,443][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:36:04,443][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:36:04,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:05,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:06,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:07,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:07,911][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:08,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:09,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:10,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:11,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:11,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:12,679][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:13,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:14,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:15,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:15,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:16,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:17,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:18,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:19,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:19,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:21,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:21,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:22,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:23,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:24,254][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:25,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:25,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:26,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:27,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:28,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:29,031][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:29,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:36:31,419][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:36:32,313][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:36:32,314][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:36:33,671][__main__][INFO] - Iteration 931 took 54s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 47m 56s. Estimated total time: 15h 13m 58s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 23s, 500 more iterations: 7h 36m 59s. +[2025-08-20 22:36:33,673][__main__][INFO] - Starting iteration 931. +[2025-08-20 22:36:56,590][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:56,591][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:56,598][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:59,024][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:59,026][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:59,032][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:36:59,034][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:36:59,035][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:36:59,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:00,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:00,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:01,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:02,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:03,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:04,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:04,885][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:05,682][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:06,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:07,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:08,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:08,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:09,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:10,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:11,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:12,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:13,348][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:14,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:14,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:15,737][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:16,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:17,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:18,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:18,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:19,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:20,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:21,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:22,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:22,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:23,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:24,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:26,077][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:37:27,020][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:37:27,022][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:37:28,479][__main__][INFO] - Iteration 932 took 54s (37.36% Gen, 62.63% Train). Generation: 20s, Training: 34s. Estimated remaining time: 46m 27s. Estimated total time: 15h 13m 25s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 42s. +[2025-08-20 22:37:28,480][__main__][INFO] - Starting iteration 932. +[2025-08-20 22:37:51,172][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:37:51,174][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:37:51,180][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:37:53,632][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:37:53,634][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:37:53,640][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:37:53,642][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:37:53,643][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:37:53,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:54,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:55,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:56,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:57,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:57,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:58,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:37:59,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:00,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:01,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:01,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:02,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:03,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:04,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:05,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:05,850][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:06,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:07,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:08,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:09,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:10,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:11,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:11,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:12,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:13,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:14,320][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:15,115][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:15,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:16,704][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:17,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:18,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:19,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:20,685][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:38:21,608][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:38:21,609][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:38:22,874][__main__][INFO] - Iteration 933 took 54s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 34s. Estimated remaining time: 38m 41s. Estimated total time: 15h 6m 33s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 39s, 500 more iterations: 7h 33m 16s. +[2025-08-20 22:38:22,876][__main__][INFO] - Starting iteration 933. +[2025-08-20 22:38:46,368][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:38:46,369][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:38:46,376][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:38:48,815][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:38:48,816][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:38:48,823][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:38:48,825][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:38:48,826][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:38:49,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:49,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:50,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:51,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:52,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:53,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:53,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:54,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:55,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:56,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:57,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:57,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:58,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:38:59,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:00,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:01,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:01,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:02,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:03,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:04,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:05,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:05,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:07,036][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:07,830][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:08,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:09,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:10,215][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:11,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:11,806][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:12,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:13,397][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:14,190][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:15,764][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:39:16,686][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:39:16,688][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:39:18,030][__main__][INFO] - Iteration 934 took 55s (38.19% Gen, 61.81% Train). Generation: 21s, Training: 34s. Estimated remaining time: 50m 27s. Estimated total time: 15h 19m 14s. Time estimates for 10 more iterations: 9m 11s, 100 more iterations: 1h 31m 55s, 500 more iterations: 7h 39m 37s. +[2025-08-20 22:39:18,032][__main__][INFO] - Starting iteration 934. +[2025-08-20 22:39:41,242][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:39:41,243][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:39:41,250][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:39:43,719][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:39:43,721][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:39:43,727][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:39:43,730][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:39:43,731][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:39:44,032][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:44,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:45,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:46,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:47,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:47,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:48,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:49,588][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:50,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:51,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:51,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:52,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:53,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:54,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:55,155][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:55,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:56,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:57,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:58,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:59,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:39:59,933][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:01,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:02,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:02,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:03,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:04,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:05,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:06,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:06,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:07,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:08,430][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:09,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:10,795][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:40:11,722][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:40:11,724][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:40:13,033][__main__][INFO] - Iteration 935 took 55s (37.75% Gen, 62.24% Train). Generation: 20s, Training: 34s. Estimated remaining time: 46m 59s. Estimated total time: 15h 16m 41s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 20s. +[2025-08-20 22:40:13,034][__main__][INFO] - Starting iteration 935. +[2025-08-20 22:40:35,889][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:40:35,891][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:40:35,897][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:40:38,392][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:40:38,396][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:40:38,405][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:40:38,408][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:40:38,408][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:40:38,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:39,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:40,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:41,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:41,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:42,708][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:43,500][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:44,295][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:45,090][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:45,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:46,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:47,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:48,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:49,063][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:49,856][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:50,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:51,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:52,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:53,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:54,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:55,144][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:55,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:56,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:57,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:58,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:59,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:40:59,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:00,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:01,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:02,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:03,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:03,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:05,563][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:41:06,548][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:41:06,550][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:41:07,925][__main__][INFO] - Iteration 936 took 54s (37.19% Gen, 62.81% Train). Generation: 20s, Training: 34s. Estimated remaining time: 44m 13s. Estimated total time: 15h 14m 50s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 25s. +[2025-08-20 22:41:07,927][__main__][INFO] - Starting iteration 936. +[2025-08-20 22:41:30,957][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:41:30,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:41:30,965][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:41:33,449][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:41:33,450][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:41:33,456][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:41:33,459][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:41:33,459][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:41:33,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:34,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:35,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:36,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:36,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:37,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:38,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:39,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:40,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:40,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:41,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:42,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:43,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:44,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:44,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:45,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:46,466][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:47,260][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:48,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:48,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:49,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:50,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:51,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:52,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:53,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:54,056][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:54,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:55,650][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:56,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:57,238][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:58,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:41:58,828][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:00,391][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:42:01,364][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:42:01,366][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:42:02,673][__main__][INFO] - Iteration 937 took 54s (37.55% Gen, 62.45% Train). Generation: 20s, Training: 34s. Estimated remaining time: 40m 54s. Estimated total time: 15h 12m 25s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 14s, 500 more iterations: 7h 36m 12s. +[2025-08-20 22:42:02,674][__main__][INFO] - Starting iteration 937. +[2025-08-20 22:42:25,745][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:42:25,746][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:42:25,752][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:42:28,216][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:42:28,218][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:42:28,224][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:42:28,226][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:42:28,226][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:42:28,526][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:29,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:30,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:30,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:31,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:32,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:33,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:34,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:34,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:35,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:36,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:37,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:38,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:38,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:39,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:40,443][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:41,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:42,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:42,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:43,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:44,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:45,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:46,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:47,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:48,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:48,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:49,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:50,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:51,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:52,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:52,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:53,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:42:55,248][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:42:56,190][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:42:56,191][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:42:57,597][__main__][INFO] - Iteration 938 took 54s (37.52% Gen, 62.47% Train). Generation: 20s, Training: 34s. Estimated remaining time: 42m 55s. Estimated total time: 15h 15m 22s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 41s. +[2025-08-20 22:42:57,598][__main__][INFO] - Starting iteration 938. +[2025-08-20 22:43:20,120][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:43:20,122][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:43:20,128][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:43:22,584][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:43:22,585][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:43:22,591][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:43:22,594][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:43:22,594][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:43:22,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:23,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:24,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:25,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:26,053][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:26,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:27,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:28,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:29,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:30,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:30,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:31,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:32,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:33,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:33,982][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:34,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:35,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:36,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:37,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:38,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:39,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:40,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:40,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:41,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:42,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:43,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:43,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:44,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:45,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:46,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:47,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:47,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:43:49,566][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:43:50,486][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:43:50,488][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:43:51,835][__main__][INFO] - Iteration 939 took 54s (36.98% Gen, 63.01% Train). Generation: 20s, Training: 34s. Estimated remaining time: 30m 36s. Estimated total time: 15h 3m 56s. Time estimates for 10 more iterations: 9m 2s, 100 more iterations: 1h 30m 23s, 500 more iterations: 7h 31m 58s. +[2025-08-20 22:43:51,837][__main__][INFO] - Starting iteration 939. +[2025-08-20 22:44:14,506][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:44:14,507][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:44:14,513][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:44:16,978][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:44:16,979][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:44:16,986][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:44:16,988][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:44:16,988][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:44:17,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:18,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:18,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:19,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:20,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:21,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:22,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:22,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:23,627][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:24,421][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:25,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:26,007][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:26,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:27,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:28,390][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:29,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:29,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:30,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:31,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:32,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:33,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:33,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:35,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:35,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:36,789][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:37,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:38,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:39,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:39,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:40,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:41,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:42,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:44:43,927][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:44:44,878][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:44:44,880][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:44:46,240][__main__][INFO] - Iteration 940 took 54s (37.15% Gen, 62.84% Train). Generation: 20s, Training: 34s. Estimated remaining time: 32m 28s. Estimated total time: 15h 6m 43s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 40s, 500 more iterations: 7h 33m 21s. +[2025-08-20 22:44:46,242][__main__][INFO] - Starting iteration 940. +[2025-08-20 22:45:08,907][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:45:08,908][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:45:08,915][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:45:11,375][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:45:11,376][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:45:11,383][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:45:11,385][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:45:11,386][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:45:11,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:12,475][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:13,270][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:14,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:14,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:15,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:16,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:17,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:18,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:18,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:19,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:20,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:21,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:22,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:22,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:24,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:24,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:25,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:26,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:27,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:28,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:28,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:29,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:30,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:31,288][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:32,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:32,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:33,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:34,471][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:35,267][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:36,064][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:36,859][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:45:38,452][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:45:39,374][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:45:39,375][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:45:40,628][__main__][INFO] - Iteration 941 took 54s (37.19% Gen, 62.81% Train). Generation: 20s, Training: 34s. Estimated remaining time: 31m 15s. Estimated total time: 15h 6m 25s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 38s, 500 more iterations: 7h 33m 12s. +[2025-08-20 22:45:40,629][__main__][INFO] - Starting iteration 941. +[2025-08-20 22:46:03,483][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:03,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:03,491][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:05,944][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:05,945][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:05,951][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:05,954][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:46:05,954][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:46:06,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:07,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:07,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:08,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:09,413][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:10,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:10,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:11,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:12,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:13,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:14,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:14,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:15,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:16,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:17,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:18,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:18,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:19,726][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:20,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:21,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:22,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:23,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:24,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:24,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:25,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:26,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:27,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:28,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:28,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:29,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:30,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:31,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:46:32,995][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:46:33,939][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:46:33,940][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:46:35,282][__main__][INFO] - Iteration 942 took 54s (37.35% Gen, 62.65% Train). Generation: 20s, Training: 34s. Estimated remaining time: 34m 48s. Estimated total time: 15h 10m 52s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 5s, 500 more iterations: 7h 35m 26s. +[2025-08-20 22:46:35,283][__main__][INFO] - Starting iteration 942. +[2025-08-20 22:46:57,958][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:57,959][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:46:57,965][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:00,408][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:00,409][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:00,416][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:00,418][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:47:00,419][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:47:00,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:01,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:02,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:03,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:03,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:04,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:05,457][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:06,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:07,040][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:07,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:08,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:09,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:10,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:11,002][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:11,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:12,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:13,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:14,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:14,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:15,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:16,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:17,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:18,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:19,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:20,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:20,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:21,753][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:22,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:23,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:24,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:24,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:25,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:27,294][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:47:28,225][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:47:28,226][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:47:29,631][__main__][INFO] - Iteration 943 took 54s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 34s. Estimated remaining time: 28m 48s. Estimated total time: 15h 5m 46s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 34s, 500 more iterations: 7h 32m 53s. +[2025-08-20 22:47:29,632][__main__][INFO] - Starting iteration 943. +[2025-08-20 22:47:52,754][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:52,756][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:52,762][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:55,213][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:55,214][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:55,220][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:47:55,222][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:47:55,223][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:47:55,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:56,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:57,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:57,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:58,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:47:59,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:00,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:01,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:01,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:02,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:03,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:04,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:05,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:05,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:06,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:07,438][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:08,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:09,030][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:09,825][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:10,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:11,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:12,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:13,618][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:14,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:15,212][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:16,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:16,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:17,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:18,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:19,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:19,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:20,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:22,387][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:48:23,338][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:48:23,340][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:48:24,560][__main__][INFO] - Iteration 944 took 54s (37.62% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 37m 29s. Estimated total time: 15h 15m 22s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 41s. +[2025-08-20 22:48:24,561][__main__][INFO] - Starting iteration 944. +[2025-08-20 22:48:47,571][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:48:47,573][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:48:47,579][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:48:50,008][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:48:50,010][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:48:50,016][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:48:50,019][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:48:50,019][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:48:50,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:51,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:51,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:52,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:53,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:54,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:55,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:55,870][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:56,665][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:57,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:58,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:59,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:48:59,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:00,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:01,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:02,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:03,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:03,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:04,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:05,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:06,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:07,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:08,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:09,138][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:09,935][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:10,729][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:11,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:12,321][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:13,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:13,916][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:14,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:15,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:17,088][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:49:18,012][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:49:18,014][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:49:19,519][__main__][INFO] - Iteration 945 took 54s (37.44% Gen, 62.56% Train). Generation: 20s, Training: 34s. Estimated remaining time: 37m 8s. Estimated total time: 15h 15m 57s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 58s. +[2025-08-20 22:49:19,520][__main__][INFO] - Starting iteration 945. +[2025-08-20 22:49:42,273][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:49:42,274][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:49:42,281][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:49:44,748][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:49:44,749][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:49:44,756][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:49:44,758][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:49:44,759][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:49:45,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:45,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:46,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:47,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:48,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:49,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:49,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:50,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:51,411][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:52,206][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:53,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:53,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:54,594][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:55,388][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:56,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:57,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:58,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:59,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:49:59,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:00,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:01,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:02,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:03,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:03,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:04,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:05,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:06,202][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:07,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:07,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:08,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:09,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:10,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:11,760][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:50:12,712][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:50:12,713][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:50:14,246][__main__][INFO] - Iteration 946 took 54s (37.08% Gen, 62.91% Train). Generation: 20s, Training: 34s. Estimated remaining time: 32m 22s. Estimated total time: 15h 12m 5s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 12s, 500 more iterations: 7h 36m 2s. +[2025-08-20 22:50:14,248][__main__][INFO] - Starting iteration 946. +[2025-08-20 22:50:37,321][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:50:37,322][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:50:37,328][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:50:39,800][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:50:39,801][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:50:39,807][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:50:39,810][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:50:39,810][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:50:40,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:40,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:41,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:42,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:43,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:44,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:44,865][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:45,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:46,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:47,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:48,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:48,835][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:49,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:50,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:51,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:52,014][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:52,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:53,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:54,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:55,197][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:55,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:57,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:58,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:58,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:50:59,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:00,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:01,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:02,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:02,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:03,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:04,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:05,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:06,891][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:51:07,829][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:51:07,830][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:51:09,050][__main__][INFO] - Iteration 947 took 54s (37.63% Gen, 62.37% Train). Generation: 20s, Training: 34s. Estimated remaining time: 32m 44s. Estimated total time: 15h 13m 22s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 20s, 500 more iterations: 7h 36m 41s. +[2025-08-20 22:51:09,052][__main__][INFO] - Starting iteration 947. +[2025-08-20 22:51:31,644][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:51:31,646][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:51:31,652][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:51:34,107][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:51:34,108][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:51:34,115][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:51:34,117][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:51:34,118][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:51:34,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:35,207][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:35,998][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:36,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:37,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:38,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:39,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:39,964][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:40,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:41,550][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:42,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:43,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:43,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:44,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:45,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:46,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:47,109][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:47,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:48,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:49,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:50,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:51,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:51,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:53,219][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:54,012][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:54,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:55,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:56,396][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:57,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:57,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:58,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:51:59,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:01,181][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:52:02,104][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:52:02,105][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:52:03,441][__main__][INFO] - Iteration 948 took 54s (37.02% Gen, 62.98% Train). Generation: 20s, Training: 34s. Estimated remaining time: 24m 55s. Estimated total time: 15h 6m 28s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 38s, 500 more iterations: 7h 33m 14s. +[2025-08-20 22:52:03,442][__main__][INFO] - Starting iteration 948. +[2025-08-20 22:52:26,073][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:52:26,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:52:26,081][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:52:28,542][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:52:28,543][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:52:28,550][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:52:28,552][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:52:28,552][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:52:28,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:29,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:30,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:31,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:32,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:32,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:33,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:34,405][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:35,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:35,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:36,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:37,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:38,377][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:39,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:39,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:40,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:41,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:42,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:43,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:43,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:45,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:45,996][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:46,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:47,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:48,387][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:49,181][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:49,975][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:50,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:51,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:52,359][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:53,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:53,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:52:55,517][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:52:56,431][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:52:56,432][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:52:57,733][__main__][INFO] - Iteration 949 took 54s (37.17% Gen, 62.83% Train). Generation: 20s, Training: 34s. Estimated remaining time: 22m 24s. Estimated total time: 15h 4m 50s. Time estimates for 10 more iterations: 9m 2s, 100 more iterations: 1h 30m 29s, 500 more iterations: 7h 32m 25s. +[2025-08-20 22:52:57,735][__main__][INFO] - Starting iteration 949. +[2025-08-20 22:53:20,371][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:53:20,372][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:53:20,379][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:53:22,860][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:53:22,861][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:53:22,868][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:53:22,870][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:53:22,871][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:53:23,169][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:23,958][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:24,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:25,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:26,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:27,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:27,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:28,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:29,514][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:30,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:31,101][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:31,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:32,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:33,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:34,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:35,076][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:35,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:36,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:37,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:38,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:39,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:40,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:41,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:41,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:42,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:43,553][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:44,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:45,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:45,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:46,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:47,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:48,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:53:49,935][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:53:50,840][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:53:50,841][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:53:52,125][__main__][INFO] - Iteration 950 took 54s (37.08% Gen, 62.92% Train). Generation: 20s, Training: 34s. Estimated remaining time: 23m 9s. Estimated total time: 15h 6m 30s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 39s, 500 more iterations: 7h 33m 15s. +[2025-08-20 22:53:52,127][__main__][INFO] - Starting iteration 950. +[2025-08-20 22:54:14,786][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:54:14,787][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:54:14,794][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:54:17,247][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:54:17,248][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:54:17,255][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:54:17,257][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:54:17,257][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:54:17,556][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:18,344][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:19,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:19,931][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:20,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:21,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:22,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:23,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:23,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:24,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:25,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:26,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:27,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:27,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:28,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:29,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:30,259][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:31,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:31,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:32,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:33,447][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:34,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:35,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:36,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:37,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:37,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:38,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:39,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:40,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:41,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:41,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:42,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:54:44,211][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:54:45,195][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:54:45,197][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:54:48,901][__main__][INFO] - Iteration 951 took 56s (35.61% Gen, 60.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 1h 1m 56s. Estimated total time: 15h 46m 13s. Time estimates for 10 more iterations: 9m 27s, 100 more iterations: 1h 34m 37s, 500 more iterations: 7h 53m 6s. +[2025-08-20 22:54:48,903][__main__][INFO] - Starting iteration 951. +[2025-08-20 22:55:11,809][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:55:11,810][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:55:11,817][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:55:14,285][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:55:14,287][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:55:14,293][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:55:14,295][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:55:14,296][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:55:14,595][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:15,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:16,182][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:16,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:17,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:18,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:19,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:20,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:20,942][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:21,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:22,529][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:23,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:24,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:24,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:25,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:27,008][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:27,801][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:28,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:29,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:30,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:30,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:31,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:32,573][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:33,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:34,161][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:34,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:35,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:36,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:37,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:38,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:38,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:39,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:55:41,340][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:55:42,233][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:55:42,235][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:55:43,587][__main__][INFO] - Iteration 952 took 54s (37.37% Gen, 62.62% Train). Generation: 20s, Training: 34s. Estimated remaining time: 26m 10s. Estimated total time: 15h 11m 23s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 8s, 500 more iterations: 7h 35m 41s. +[2025-08-20 22:55:43,588][__main__][INFO] - Starting iteration 952. +[2025-08-20 22:56:06,276][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:56:06,277][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:56:06,284][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:56:08,770][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:56:08,772][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:56:08,778][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:56:08,780][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:56:08,781][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:56:09,084][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:09,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:10,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:11,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:12,252][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:13,046][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:13,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:14,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:15,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:16,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:17,017][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:17,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:18,606][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:19,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:20,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:20,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:21,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:22,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:23,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:24,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:24,966][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:25,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:27,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:27,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:28,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:29,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:30,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:31,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:31,861][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:32,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:33,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:34,242][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:56:35,857][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:56:36,806][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:56:36,807][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:56:38,013][__main__][INFO] - Iteration 953 took 54s (37.12% Gen, 62.88% Train). Generation: 20s, Training: 34s. Estimated remaining time: 20m 57s. Estimated total time: 15h 7m 4s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 42s, 500 more iterations: 7h 33m 32s. +[2025-08-20 22:56:38,014][__main__][INFO] - Starting iteration 953. +[2025-08-20 22:57:00,626][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:00,628][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:00,634][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:03,110][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:03,112][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:03,118][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:03,120][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:57:03,121][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:57:03,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:04,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:05,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:05,794][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:06,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:07,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:08,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:08,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:09,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:10,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:11,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:12,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:12,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:13,742][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:14,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:15,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:16,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:16,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:17,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:18,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:19,306][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:20,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:21,333][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:22,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:22,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:23,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:24,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:25,305][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:26,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:26,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:27,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:28,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:30,084][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:57:31,013][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:57:31,014][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:57:32,457][__main__][INFO] - Iteration 954 took 54s (36.97% Gen, 63.03% Train). Generation: 20s, Training: 34s. Estimated remaining time: 20m 20s. Estimated total time: 15h 7m 21s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 44s, 500 more iterations: 7h 33m 40s. +[2025-08-20 22:57:32,458][__main__][INFO] - Starting iteration 954. +[2025-08-20 22:57:54,958][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:54,960][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:54,966][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:57,428][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:57,430][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:57,436][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:57:57,438][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:57:57,439][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:57:57,738][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:58,530][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:57:59,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:00,114][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:00,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:01,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:02,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:03,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:04,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:04,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:05,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:06,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:07,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:08,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:08,853][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:09,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:10,448][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:11,243][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:12,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:13,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:14,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:14,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:15,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:16,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:17,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:18,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:18,903][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:19,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:20,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:21,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:22,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:22,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:24,446][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:58:25,357][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:58:25,359][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:58:26,822][__main__][INFO] - Iteration 955 took 54s (36.90% Gen, 63.10% Train). Generation: 20s, Training: 34s. Estimated remaining time: 18m 8s. Estimated total time: 15h 6m 4s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 36s, 500 more iterations: 7h 33m 2s. +[2025-08-20 22:58:26,824][__main__][INFO] - Starting iteration 955. +[2025-08-20 22:58:49,492][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:58:49,494][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:58:49,500][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:58:51,972][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:58:51,973][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:58:51,980][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:58:51,982][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:58:51,983][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:58:52,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:53,074][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:53,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:54,658][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:55,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:56,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:57,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:57,836][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:58,633][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:58:59,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:00,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:01,016][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:01,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:02,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:03,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:04,198][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:04,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:05,791][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:06,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:07,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:08,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:08,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:09,771][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:10,567][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:11,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:12,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:13,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:14,230][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:15,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:15,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:16,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:17,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:19,026][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 22:59:19,934][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 22:59:19,936][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 22:59:21,197][__main__][INFO] - Iteration 956 took 54s (37.16% Gen, 62.84% Train). Generation: 20s, Training: 34s. Estimated remaining time: 17m 22s. Estimated total time: 15h 6m 12s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 37s, 500 more iterations: 7h 33m 6s. +[2025-08-20 22:59:21,198][__main__][INFO] - Starting iteration 956. +[2025-08-20 22:59:44,933][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:59:44,934][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:59:44,940][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:59:47,418][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:59:47,420][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:59:47,426][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 22:59:47,428][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 22:59:47,429][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 22:59:47,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:48,518][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:49,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:50,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:50,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:51,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:52,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:53,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:54,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:54,877][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:55,674][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:56,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:57,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:58,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:58,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 22:59:59,651][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:00,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:01,711][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:02,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:03,299][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:04,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:04,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:05,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:06,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:07,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:08,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:08,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:09,662][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:10,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:11,253][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:12,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:12,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:14,419][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:00:15,358][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:00:15,359][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:00:16,720][__main__][INFO] - Iteration 957 took 55s (38.31% Gen, 61.69% Train). Generation: 21s, Training: 34s. Estimated remaining time: 35m 35s. Estimated total time: 15h 25m 21s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 32s, 500 more iterations: 7h 42m 40s. +[2025-08-20 23:00:16,721][__main__][INFO] - Starting iteration 957. +[2025-08-20 23:00:39,443][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:00:39,444][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:00:39,451][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:00:41,896][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:00:41,898][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:00:41,904][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:00:41,907][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:00:41,907][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:00:42,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:43,000][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:43,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:44,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:45,382][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:46,178][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:46,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:47,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:48,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:49,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:50,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:50,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:51,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:52,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:53,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:54,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:54,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:55,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:56,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:57,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:58,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:00:59,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:00,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:01,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:01,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:02,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:03,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:04,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:04,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:05,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:06,576][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:07,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:08,944][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:01:09,827][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:01:09,828][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:01:11,163][__main__][INFO] - Iteration 958 took 54s (37.27% Gen, 62.73% Train). Generation: 20s, Training: 34s. Estimated remaining time: 16m 41s. Estimated total time: 15h 7m 21s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 44s, 500 more iterations: 7h 33m 40s. +[2025-08-20 23:01:11,165][__main__][INFO] - Starting iteration 958. +[2025-08-20 23:01:34,296][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:01:34,298][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:01:34,304][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:01:36,742][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:01:36,744][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:01:36,750][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:01:36,752][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:01:36,753][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:01:37,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:37,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:38,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:39,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:40,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:41,010][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:41,803][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:42,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:43,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:44,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:44,978][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:45,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:46,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:47,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:48,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:48,945][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:49,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:50,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:51,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:52,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:52,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:53,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:54,506][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:55,785][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:56,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:57,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:58,168][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:58,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:01:59,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:00,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:01,345][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:02,139][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:03,800][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:02:04,701][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:02:04,702][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:02:06,068][__main__][INFO] - Iteration 959 took 54s (37.71% Gen, 62.29% Train). Generation: 20s, Training: 34s. Estimated remaining time: 23m 28s. Estimated total time: 15h 15m 3s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 30s, 500 more iterations: 7h 37m 31s. +[2025-08-20 23:02:06,070][__main__][INFO] - Starting iteration 959. +[2025-08-20 23:02:28,747][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:02:28,749][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:02:28,755][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:02:31,211][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:02:31,212][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:02:31,218][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:02:31,221][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:02:31,221][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:02:31,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:32,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:33,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:33,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:34,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:35,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:36,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:37,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:37,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:38,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:39,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:40,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:41,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:41,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:42,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:43,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:44,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:45,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:45,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:47,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:47,846][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:48,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:49,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:50,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:51,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:51,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:52,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:53,403][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:54,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:54,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:55,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:56,584][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:02:58,152][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:02:59,090][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:02:59,092][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:03:00,390][__main__][INFO] - Iteration 960 took 54s (37.23% Gen, 62.77% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12m 50s. Estimated total time: 15h 5m 19s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 31s, 500 more iterations: 7h 32m 39s. +[2025-08-20 23:03:00,391][__main__][INFO] - Starting iteration 960. +[2025-08-20 23:03:23,604][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:03:23,605][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:03:23,611][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:03:26,069][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:03:26,071][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:03:26,077][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:03:26,079][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:03:26,080][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:03:26,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:27,172][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:27,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:28,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:29,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:30,352][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:31,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:31,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:32,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:33,535][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:34,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:35,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:35,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:36,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:37,511][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:38,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:39,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:40,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:41,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:42,009][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:42,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:43,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:44,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:45,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:45,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:46,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:47,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:48,369][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:49,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:49,963][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:50,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:51,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:03:53,161][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:03:54,077][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:03:54,078][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:03:55,347][__main__][INFO] - Iteration 961 took 54s (37.74% Gen, 62.26% Train). Generation: 20s, Training: 34s. Estimated remaining time: 22m 31s. Estimated total time: 15h 15m 55s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 35s, 500 more iterations: 7h 37m 57s. +[2025-08-20 23:03:55,348][__main__][INFO] - Starting iteration 961. +[2025-08-20 23:04:17,914][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:04:17,916][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:04:17,922][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:04:20,344][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:04:20,346][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:04:20,352][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:04:20,355][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:04:20,355][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:04:20,654][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:21,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:22,237][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:23,033][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:23,827][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:24,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:25,414][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:26,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:27,004][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:27,798][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:28,593][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:29,389][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:30,183][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:30,976][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:31,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:32,570][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:33,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:34,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:34,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:35,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:36,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:37,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:38,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:39,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:40,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:41,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:41,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:42,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:43,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:44,218][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:45,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:45,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:04:47,443][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:04:48,323][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:04:48,324][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:04:49,607][__main__][INFO] - Iteration 962 took 54s (37.11% Gen, 62.89% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9m 59s. Estimated total time: 15h 4m 18s. Time estimates for 10 more iterations: 9m 2s, 100 more iterations: 1h 30m 25s, 500 more iterations: 7h 32m 9s. +[2025-08-20 23:04:49,609][__main__][INFO] - Starting iteration 962. +[2025-08-20 23:05:12,612][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:05:12,613][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:05:12,620][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:05:15,091][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:05:15,092][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:05:15,098][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:05:15,101][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:05:15,101][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:05:15,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:16,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:16,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:17,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:18,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:19,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:20,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:20,954][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:21,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:22,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:23,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:24,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:24,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:25,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:26,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:27,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:28,561][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:29,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:30,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:30,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:31,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:32,532][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:33,327][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:34,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:34,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:35,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:36,499][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:37,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:38,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:38,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:39,675][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:40,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:05:42,048][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:05:42,960][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:05:42,961][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:05:44,407][__main__][INFO] - Iteration 963 took 54s (37.48% Gen, 62.52% Train). Generation: 20s, Training: 34s. Estimated remaining time: 18m 4s. Estimated total time: 15h 13m 17s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 19s, 500 more iterations: 7h 36m 38s. +[2025-08-20 23:05:44,408][__main__][INFO] - Starting iteration 963. +[2025-08-20 23:06:06,980][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:06:06,981][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:06:06,988][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:06:09,440][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:06:09,441][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:06:09,448][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:06:09,451][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:06:09,451][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:06:09,752][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:10,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:11,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:12,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:12,925][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:13,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:14,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:15,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:16,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:16,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:17,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:18,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:19,283][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:20,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:20,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:21,666][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:22,459][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:23,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:24,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:25,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:26,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:26,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:27,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:28,568][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:29,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:30,154][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:30,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:31,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:32,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:33,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:34,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:34,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:06:36,526][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:06:37,489][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:06:37,491][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:06:38,727][__main__][INFO] - Iteration 964 took 54s (37.05% Gen, 62.95% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9m 10s. Estimated total time: 15h 5m 18s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 31s, 500 more iterations: 7h 32m 39s. +[2025-08-20 23:06:38,728][__main__][INFO] - Starting iteration 964. +[2025-08-20 23:07:01,424][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:01,425][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:01,432][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:03,879][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:03,881][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:03,887][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:03,890][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:07:03,890][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:07:04,189][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:04,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:05,774][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:06,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:07,362][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:08,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:08,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:09,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:10,541][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:11,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:12,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:12,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:13,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:14,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:15,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:16,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:16,901][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:17,698][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:18,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:19,289][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:20,083][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:20,878][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:21,673][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:22,465][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:23,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:24,054][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:25,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:26,220][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:27,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:27,808][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:28,600][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:29,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:31,017][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:07:31,992][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:07:31,993][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:07:33,317][__main__][INFO] - Iteration 965 took 54s (37.10% Gen, 62.90% Train). Generation: 20s, Training: 34s. Estimated remaining time: 12m 46s. Estimated total time: 15h 9m 48s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 58s, 500 more iterations: 7h 34m 54s. +[2025-08-20 23:07:33,319][__main__][INFO] - Starting iteration 965. +[2025-08-20 23:07:56,053][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:56,054][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:56,061][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:58,500][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:58,502][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:58,509][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:07:58,511][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:07:58,512][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:07:58,810][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:07:59,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:00,395][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:01,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:01,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:02,776][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:03,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:04,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:05,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:05,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:06,747][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:07,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:08,339][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:09,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:09,927][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:10,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:11,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:12,315][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:13,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:14,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:15,149][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:15,941][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:16,736][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:17,528][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:18,322][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:19,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:19,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:20,702][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:21,498][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:22,293][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:23,086][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:23,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:25,471][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:08:26,402][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:08:26,404][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:08:27,684][__main__][INFO] - Iteration 966 took 54s (37.29% Gen, 62.71% Train). Generation: 20s, Training: 34s. Estimated remaining time: 8m 8s. Estimated total time: 15h 6m 5s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 36s, 500 more iterations: 7h 33m 2s. +[2025-08-20 23:08:27,686][__main__][INFO] - Starting iteration 966. +[2025-08-20 23:08:50,576][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:08:50,577][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:08:50,583][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:08:53,050][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:08:53,051][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:08:53,058][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:08:53,060][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:08:53,061][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:08:53,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:54,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:54,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:55,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:56,533][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:57,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:58,126][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:58,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:08:59,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:00,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:01,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:02,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:02,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:03,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:04,487][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:05,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:06,082][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:06,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:07,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:08,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:09,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:10,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:11,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:12,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:12,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:13,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:14,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:15,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:16,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:16,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:17,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:18,488][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:20,099][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:09:21,049][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:09:21,051][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:09:22,452][__main__][INFO] - Iteration 967 took 54s (37.28% Gen, 62.72% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13m 54s. Estimated total time: 15h 12m 46s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 16s, 500 more iterations: 7h 36m 23s. +[2025-08-20 23:09:22,454][__main__][INFO] - Starting iteration 967. +[2025-08-20 23:09:45,523][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:09:45,525][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:09:45,532][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:09:47,991][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:09:47,993][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:09:47,999][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:09:48,002][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:09:48,003][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:09:48,301][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:49,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:49,880][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:50,671][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:51,464][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:52,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:53,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:53,843][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:54,636][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:55,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:56,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:57,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:57,809][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:58,604][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:09:59,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:00,191][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:00,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:01,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:02,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:03,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:04,575][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:05,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:06,159][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:06,950][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:07,744][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:08,536][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:09,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:10,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:10,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:11,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:12,503][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:13,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:14,896][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:10:15,883][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:10:15,884][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:10:17,235][__main__][INFO] - Iteration 968 took 54s (37.61% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13m 14s. Estimated total time: 15h 13m 0s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 18s, 500 more iterations: 7h 36m 30s. +[2025-08-20 23:10:17,236][__main__][INFO] - Starting iteration 968. +[2025-08-20 23:10:40,474][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:10:40,475][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:10:40,482][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:10:42,924][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:10:42,925][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:10:42,932][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:10:42,934][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:10:42,934][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:10:43,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:44,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:44,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:45,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:46,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:47,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:47,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:48,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:49,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:50,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:51,175][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:51,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:52,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:53,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:54,357][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:55,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:55,949][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:56,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:57,538][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:58,332][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:59,129][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:10:59,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:00,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:01,999][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:02,792][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:03,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:04,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:05,179][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:05,972][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:06,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:07,562][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:08,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:09,926][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:11:10,848][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:11:10,849][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:11:12,119][__main__][INFO] - Iteration 969 took 54s (37.90% Gen, 62.10% Train). Generation: 20s, Training: 34s. Estimated remaining time: 14m 1s. Estimated total time: 15h 14m 42s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 21s. +[2025-08-20 23:11:12,120][__main__][INFO] - Starting iteration 969. +[2025-08-20 23:11:35,161][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:11:35,162][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:11:35,169][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:11:37,631][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:11:37,632][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:11:37,638][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:11:37,640][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:11:37,641][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:11:37,940][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:38,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:39,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:40,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:41,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:41,906][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:42,700][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:43,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:44,292][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:45,085][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:45,879][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:46,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:47,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:48,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:49,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:49,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:50,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:51,449][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:52,244][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:53,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:54,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:55,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:55,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:56,764][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:57,558][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:58,353][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:59,150][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:11:59,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:00,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:01,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:02,328][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:03,122][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:04,694][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:12:05,602][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:12:05,603][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:12:06,998][__main__][INFO] - Iteration 970 took 54s (37.52% Gen, 62.48% Train). Generation: 20s, Training: 34s. Estimated remaining time: 13m 1s. Estimated total time: 15h 14m 37s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 27s, 500 more iterations: 7h 37m 18s. +[2025-08-20 23:12:06,999][__main__][INFO] - Starting iteration 970. +[2025-08-20 23:12:29,777][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:12:29,778][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:12:29,784][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:12:32,249][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:12:32,250][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:12:32,257][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:12:32,259][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:12:32,260][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:12:32,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:33,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:34,146][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:34,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:35,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:36,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:37,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:38,112][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:38,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:39,699][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:40,494][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:41,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:42,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:43,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:44,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:44,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:45,697][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:46,493][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:47,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:48,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:48,874][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:49,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:50,460][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:51,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:52,050][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:52,844][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:53,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:54,433][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:55,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:56,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:56,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:57,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:12:59,227][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:13:00,164][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:13:00,165][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:13:01,579][__main__][INFO] - Iteration 971 took 54s (37.25% Gen, 62.75% Train). Generation: 20s, Training: 34s. Estimated remaining time: 7m 8s. Estimated total time: 15h 9m 39s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 57s, 500 more iterations: 7h 34m 49s. +[2025-08-20 23:13:01,581][__main__][INFO] - Starting iteration 971. +[2025-08-20 23:13:25,308][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:13:25,310][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:13:25,316][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:13:27,808][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:13:27,809][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:13:27,816][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:13:27,819][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:13:27,819][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:13:28,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:28,915][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:29,706][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:30,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:31,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:32,088][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:32,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:33,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:34,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:35,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:36,059][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:36,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:37,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:38,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:39,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:40,035][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:40,829][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:41,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:42,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:43,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:44,462][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:45,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:46,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:46,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:47,639][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:48,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:49,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:50,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:50,818][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:51,614][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:52,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:53,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8950 tokens. +[2025-08-20 23:13:54,790][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:13:55,771][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:13:55,772][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:13:57,135][__main__][INFO] - Iteration 972 took 55s (38.20% Gen, 61.79% Train). Generation: 21s, Training: 34s. Estimated remaining time: 22m 27s. Estimated total time: 15h 25m 53s. Time estimates for 10 more iterations: 9m 15s, 100 more iterations: 1h 32m 35s, 500 more iterations: 7h 42m 56s. +[2025-08-20 23:13:57,136][__main__][INFO] - Starting iteration 972. +[2025-08-20 23:14:19,781][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:14:19,783][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:14:19,789][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:14:22,259][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:14:22,261][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:14:22,267][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:14:22,269][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:14:22,270][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:14:22,569][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:23,361][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:24,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:24,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:25,740][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:26,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:27,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:28,120][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:28,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:29,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:30,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:31,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:32,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:32,882][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:33,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:34,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:35,266][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:36,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:36,854][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:37,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:38,440][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:39,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:40,025][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:41,336][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:42,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:42,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:43,717][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:44,509][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:45,302][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:46,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:46,893][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:47,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:14:49,308][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:14:50,231][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:14:50,232][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:14:51,464][__main__][INFO] - Iteration 973 took 54s (37.15% Gen, 62.84% Train). Generation: 20s, Training: 34s. Estimated remaining time: 1m 7s. Estimated total time: 15h 5m 27s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 32s, 500 more iterations: 7h 32m 43s. +[2025-08-20 23:14:51,466][__main__][INFO] - Starting iteration 973. +[2025-08-20 23:15:14,660][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:15:14,662][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:15:14,668][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:15:17,128][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:15:17,129][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:15:17,135][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:15:17,138][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:15:17,138][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:15:17,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:18,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:19,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:19,815][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:20,610][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:21,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:22,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:22,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:23,788][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:24,585][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:25,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:26,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:26,969][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:27,766][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:28,564][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:29,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:30,153][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:30,947][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:31,743][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:32,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:33,330][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:34,125][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:34,922][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:36,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:36,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:37,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:38,521][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:39,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:40,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:40,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:41,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:42,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:15:44,115][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:15:45,060][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:15:45,061][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:15:46,356][__main__][INFO] - Iteration 974 took 54s (37.77% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 9m 34s. Estimated total time: 15h 14m 49s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 24s. +[2025-08-20 23:15:46,357][__main__][INFO] - Starting iteration 974. +[2025-08-20 23:16:10,070][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:16:10,071][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:16:10,078][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:16:12,559][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:16:12,561][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:16:12,568][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:16:12,570][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:16:12,570][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:16:12,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:13,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:14,453][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:15,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:16,044][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:16,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:17,632][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:18,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:19,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:20,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:20,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:21,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:22,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:23,195][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:23,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:24,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:25,580][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:26,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:27,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:27,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:28,754][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:29,549][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:30,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:31,141][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:31,934][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:32,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:33,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:34,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:35,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:36,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:37,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:37,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:16:39,540][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:16:40,421][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:16:40,423][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:16:41,846][__main__][INFO] - Iteration 975 took 55s (38.27% Gen, 61.73% Train). Generation: 21s, Training: 34s. Estimated remaining time: 18m 37s. Estimated total time: 15h 24m 48s. Time estimates for 10 more iterations: 9m 14s, 100 more iterations: 1h 32m 28s, 500 more iterations: 7h 42m 24s. +[2025-08-20 23:16:41,847][__main__][INFO] - Starting iteration 975. +[2025-08-20 23:17:04,564][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:04,565][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:04,571][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:07,031][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:07,032][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:07,039][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:07,041][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:17:07,042][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:17:07,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:08,132][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:08,924][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:09,716][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:10,513][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:11,307][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:12,100][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:12,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:13,687][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:14,480][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:15,274][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:16,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:16,862][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:17,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:18,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:19,246][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:20,038][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:20,831][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:21,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:22,417][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:23,211][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:24,006][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:25,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:26,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:26,980][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:27,769][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:28,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:29,355][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:30,147][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:30,939][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:31,730][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:32,525][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:17:34,096][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:17:35,337][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:17:35,339][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:17:36,606][__main__][INFO] - Iteration 976 took 54s (36.99% Gen, 63.01% Train). Generation: 20s, Training: 34s. Estimated remaining time: 5m 33s. Estimated total time: 15h 12m 38s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 19s. +[2025-08-20 23:17:36,608][__main__][INFO] - Starting iteration 976. +[2025-08-20 23:17:59,422][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:59,424][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:17:59,430][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:01,885][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:01,886][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:01,893][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:01,895][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:18:01,895][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:18:02,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:02,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:03,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:04,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:05,364][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:06,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:06,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:07,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:08,539][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:09,334][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:10,128][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:10,921][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:11,715][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:12,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:13,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:14,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:15,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:16,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:16,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:17,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:18,484][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:19,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:20,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:20,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:21,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:22,452][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:23,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:24,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:24,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:25,630][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:26,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:27,217][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:28,890][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:18:29,801][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:18:29,802][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:18:31,071][__main__][INFO] - Iteration 977 took 54s (37.40% Gen, 62.59% Train). Generation: 20s, Training: 34s. Estimated remaining time: -17s. Estimated total time: 15h 7m 42s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 46s, 500 more iterations: 7h 33m 51s. +[2025-08-20 23:18:31,074][__main__][INFO] - Starting iteration 977. +[2025-08-20 23:18:54,072][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:54,073][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:54,080][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:56,538][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:56,539][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:56,546][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:18:56,548][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:18:56,549][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:18:56,849][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:57,640][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:58,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:18:59,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:00,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:00,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:01,609][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:02,402][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:03,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:03,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:04,790][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:05,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:06,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:07,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:07,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:08,762][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:09,557][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:10,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:11,142][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:11,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:12,732][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:14,051][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:14,845][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:15,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:16,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:17,229][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:18,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:18,816][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:19,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:20,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:21,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:21,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:23,586][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:19:24,512][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:19:24,514][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:19:25,820][__main__][INFO] - Iteration 978 took 54s (37.54% Gen, 62.46% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3m 30s. Estimated total time: 15h 12m 25s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 14s, 500 more iterations: 7h 36m 12s. +[2025-08-20 23:19:25,821][__main__][INFO] - Starting iteration 978. +[2025-08-20 23:19:49,002][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:19:49,004][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:19:49,010][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:19:51,487][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:19:51,489][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:19:51,495][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:19:51,497][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:19:51,498][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:19:51,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:52,587][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:53,381][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:54,174][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:54,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:55,763][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:56,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:57,354][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:58,148][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:58,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:19:59,741][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:00,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:01,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:02,123][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:02,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:03,713][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:04,505][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:05,300][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:06,095][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:06,888][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:07,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:08,977][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:09,772][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:10,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:11,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:12,157][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:12,952][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:13,746][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:14,540][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:15,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:16,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:16,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:18,545][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:20:19,472][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:20:19,474][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:20:20,705][__main__][INFO] - Iteration 979 took 54s (37.76% Gen, 62.23% Train). Generation: 20s, Training: 34s. Estimated remaining time: 4m 54s. Estimated total time: 15h 14m 43s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 28s, 500 more iterations: 7h 37m 21s. +[2025-08-20 23:20:20,707][__main__][INFO] - Starting iteration 979. +[2025-08-20 23:20:43,412][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:20:43,414][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:20:43,420][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:20:45,887][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:20:45,888][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:20:45,895][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:20:45,897][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:20:45,898][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:20:46,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:46,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:47,775][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:48,566][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:49,360][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:50,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:50,943][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:51,735][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:52,527][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:53,323][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:54,117][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:54,909][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:55,703][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:56,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:57,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:58,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:58,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:20:59,664][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:00,458][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:01,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:02,483][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:03,278][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:04,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:04,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:05,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:06,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:07,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:08,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:08,839][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:09,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:10,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:11,221][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:12,836][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:21:13,789][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:21:13,791][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:21:15,098][__main__][INFO] - Iteration 980 took 54s (37.22% Gen, 62.78% Train). Generation: 20s, Training: 34s. Estimated remaining time: -253s. Estimated total time: 15h 6m 30s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 39s, 500 more iterations: 7h 33m 15s. +[2025-08-20 23:21:15,099][__main__][INFO] - Starting iteration 980. +[2025-08-20 23:21:38,335][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:21:38,337][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:21:38,343][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:21:40,800][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:21:40,801][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:21:40,808][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:21:40,810][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:21:40,811][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:21:41,110][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:41,904][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:42,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:43,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:44,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:45,081][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:45,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:46,669][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:47,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:48,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:49,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:49,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:50,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:51,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:52,234][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:53,027][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:53,823][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:54,616][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:55,409][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:56,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:57,001][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:58,241][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:59,034][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:21:59,826][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:00,622][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:01,415][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:02,208][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:03,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:03,797][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:04,589][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:05,383][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:06,180][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:07,789][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:22:08,729][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:22:08,730][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:22:10,026][__main__][INFO] - Iteration 981 took 54s (37.85% Gen, 62.15% Train). Generation: 20s, Training: 34s. Estimated remaining time: 3m 47s. Estimated total time: 15h 15m 26s. Time estimates for 10 more iterations: 9m 9s, 100 more iterations: 1h 31m 32s, 500 more iterations: 7h 37m 43s. +[2025-08-20 23:22:10,028][__main__][INFO] - Starting iteration 981. +[2025-08-20 23:22:33,218][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:22:33,219][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:22:33,226][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:22:35,678][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:22:35,680][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:22:35,686][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:22:35,689][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:22:35,689][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:22:35,988][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:36,777][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:37,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:38,368][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:39,160][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:39,953][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:40,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:41,544][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:42,338][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:43,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:43,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:44,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:45,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:46,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:47,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:47,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:48,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:49,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:50,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:51,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:51,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:52,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:53,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:54,249][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:55,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:56,431][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:57,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:58,023][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:58,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:22:59,611][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:00,406][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:01,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:02,786][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:23:03,716][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:23:03,717][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:23:04,924][__main__][INFO] - Iteration 982 took 54s (37.80% Gen, 62.20% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2m 21s. Estimated total time: 15h 14m 55s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 29s, 500 more iterations: 7h 37m 27s. +[2025-08-20 23:23:04,925][__main__][INFO] - Starting iteration 982. +[2025-08-20 23:23:27,720][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:23:27,722][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:23:27,728][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:23:30,203][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:23:30,205][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:23:30,211][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:23:30,213][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:23:30,214][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:23:30,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:31,311][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:32,102][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:32,895][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:33,690][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:34,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:35,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:36,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:36,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:37,659][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:38,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:39,251][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:40,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:40,837][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:41,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:42,424][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:43,216][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:44,011][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:44,805][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:45,598][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:46,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:47,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:48,481][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:49,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:50,069][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:50,863][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:51,656][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:52,450][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:53,247][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:54,041][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:54,834][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:55,629][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:23:57,225][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:23:58,121][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:23:58,122][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:23:59,461][__main__][INFO] - Iteration 983 took 54s (37.28% Gen, 62.72% Train). Generation: 20s, Training: 34s. Estimated remaining time: -273s. Estimated total time: 15h 8m 54s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 53s, 500 more iterations: 7h 34m 27s. +[2025-08-20 23:23:59,462][__main__][INFO] - Starting iteration 983. +[2025-08-20 23:24:22,073][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:24:22,075][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:24:22,081][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:24:24,542][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:24:24,543][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:24:24,549][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:24:24,551][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:24:24,552][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:24:24,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:25,641][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:26,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:27,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:28,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:28,813][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:29,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:30,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:31,196][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:31,990][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:32,783][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:33,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:34,373][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:35,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:36,437][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:37,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:38,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:38,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:39,608][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:40,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:41,193][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:41,987][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:42,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:43,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:44,365][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:45,158][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:45,951][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:46,745][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:47,537][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:48,331][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:49,127][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:49,920][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:24:51,494][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:24:52,418][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:24:52,420][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:24:53,698][__main__][INFO] - Iteration 984 took 54s (37.19% Gen, 62.81% Train). Generation: 20s, Training: 34s. Estimated remaining time: -627s. Estimated total time: 15h 3m 55s. Time estimates for 10 more iterations: 9m 2s, 100 more iterations: 1h 30m 23s, 500 more iterations: 7h 31m 57s. +[2025-08-20 23:24:53,699][__main__][INFO] - Starting iteration 984. +[2025-08-20 23:25:16,378][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:25:16,379][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:25:16,386][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:25:18,878][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:25:18,879][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:25:18,885][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:25:18,888][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:25:18,888][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:25:19,187][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:19,979][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:20,770][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:21,563][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:22,358][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:23,151][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:23,944][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:24,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:25,534][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:26,326][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:27,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:27,917][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:28,710][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:29,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:30,297][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:31,089][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:31,881][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:32,676][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:33,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:34,263][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:35,058][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:36,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:37,140][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:37,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:38,731][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:39,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:40,316][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:41,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:41,908][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:42,701][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:43,495][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:44,291][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:25:45,943][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:25:46,870][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:25:46,871][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:25:48,095][__main__][INFO] - Iteration 985 took 54s (37.16% Gen, 62.84% Train). Generation: 20s, Training: 34s. Estimated remaining time: -521s. Estimated total time: 15h 6m 35s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 39s, 500 more iterations: 7h 33m 17s. +[2025-08-20 23:25:48,096][__main__][INFO] - Starting iteration 985. +[2025-08-20 23:26:10,842][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:26:10,843][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:26:10,850][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:26:13,326][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:26:13,328][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:26:13,334][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:26:13,337][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:26:13,338][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:26:13,637][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:14,427][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:15,222][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:16,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:16,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:17,603][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:18,398][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:19,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:19,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:20,782][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:21,577][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:22,370][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:23,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:23,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:24,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:25,542][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:26,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:27,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:28,346][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:29,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:29,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:30,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:31,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:32,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:33,097][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:33,889][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:34,680][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:35,474][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:36,265][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:37,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:37,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:38,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:26:40,222][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:26:41,166][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:26:41,167][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:26:42,459][__main__][INFO] - Iteration 986 took 54s (37.33% Gen, 62.67% Train). Generation: 20s, Training: 34s. Estimated remaining time: -608s. Estimated total time: 15h 6m 2s. Time estimates for 10 more iterations: 9m 3s, 100 more iterations: 1h 30m 36s, 500 more iterations: 7h 33m 1s. +[2025-08-20 23:26:42,460][__main__][INFO] - Starting iteration 986. +[2025-08-20 23:27:05,714][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:27:05,715][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:27:05,722][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:27:08,172][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:27:08,173][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:27:08,180][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:27:08,182][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:27:08,182][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:27:08,491][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:09,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:10,071][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:10,864][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:11,655][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:12,445][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:13,239][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:14,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:14,824][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:15,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:16,408][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:17,203][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:17,995][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:18,787][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:19,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:20,375][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:21,166][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:21,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:23,232][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:24,024][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:24,820][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:25,612][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:26,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:27,200][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:27,994][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:28,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:29,581][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:30,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:31,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:31,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:32,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:33,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:27:35,095][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:27:36,017][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:27:36,019][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:27:37,289][__main__][INFO] - Iteration 987 took 54s (37.96% Gen, 62.04% Train). Generation: 20s, Training: 34s. Estimated remaining time: -197s. Estimated total time: 15h 13m 48s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 22s, 500 more iterations: 7h 36m 54s. +[2025-08-20 23:27:37,291][__main__][INFO] - Starting iteration 987. +[2025-08-20 23:28:00,411][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:00,412][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:00,419][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:02,873][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:02,874][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:02,881][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:02,883][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:28:02,883][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:28:03,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:03,974][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:04,768][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:05,559][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:06,350][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:07,145][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:07,936][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:08,727][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:09,520][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:10,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:11,106][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:11,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:12,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:13,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:14,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:15,073][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:15,867][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:16,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:17,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:18,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:19,045][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:19,840][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:21,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:22,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:22,802][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:23,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:24,391][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:25,185][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:25,983][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:26,778][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:27,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:28,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:30,022][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:28:31,216][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:28:31,218][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:28:32,503][__main__][INFO] - Iteration 988 took 55s (37.45% Gen, 62.55% Train). Generation: 20s, Training: 34s. Estimated remaining time: 2m 9s. Estimated total time: 15h 20m 11s. Time estimates for 10 more iterations: 9m 12s, 100 more iterations: 1h 32m 1s, 500 more iterations: 7h 40m 5s. +[2025-08-20 23:28:32,504][__main__][INFO] - Starting iteration 988. +[2025-08-20 23:28:55,554][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:55,555][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:55,561][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:58,051][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:58,053][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:58,059][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:28:58,062][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:28:58,062][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:28:58,372][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:59,164][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:28:59,955][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:00,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:01,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:02,335][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:03,131][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:03,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:04,718][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:05,512][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:06,308][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:07,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:07,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:08,691][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:09,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:10,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:11,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:11,873][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:12,670][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:13,918][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:14,712][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:15,508][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:16,304][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:17,098][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:17,891][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:18,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:19,486][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:20,281][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:21,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:21,869][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:22,668][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:23,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:25,050][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:29:25,924][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:29:25,925][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:29:27,262][__main__][INFO] - Iteration 989 took 54s (37.61% Gen, 62.39% Train). Generation: 20s, Training: 34s. Estimated remaining time: -378s. Estimated total time: 15h 12m 37s. Time estimates for 10 more iterations: 9m 7s, 100 more iterations: 1h 31m 15s, 500 more iterations: 7h 36m 18s. +[2025-08-20 23:29:27,263][__main__][INFO] - Starting iteration 989. +[2025-08-20 23:29:50,027][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:29:50,028][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:29:50,034][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:29:52,489][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:29:52,491][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:29:52,497][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:29:52,500][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:29:52,500][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:29:52,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:53,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:54,385][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:55,177][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:55,973][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:56,767][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:57,560][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:58,356][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:59,152][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:29:59,946][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:00,739][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:01,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:02,325][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:03,119][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:03,913][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:04,709][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:05,502][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:06,296][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:07,686][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:08,479][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:09,273][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:10,070][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:10,866][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:11,660][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:12,455][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:13,250][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:14,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:14,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:15,634][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:16,429][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:17,226][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:18,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:19,634][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:30:20,595][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:30:20,596][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:30:21,963][__main__][INFO] - Iteration 990 took 54s (37.13% Gen, 62.87% Train). Generation: 20s, Training: 34s. Estimated remaining time: -491s. Estimated total time: 15h 11m 39s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 9s, 500 more iterations: 7h 35m 49s. +[2025-08-20 23:30:21,965][__main__][INFO] - Starting iteration 990. +[2025-08-20 23:30:44,667][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:30:44,668][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:30:44,675][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:30:47,156][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:30:47,157][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:30:47,163][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:30:47,166][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:30:47,166][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:30:47,472][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:48,262][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:49,057][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:49,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:50,643][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:51,439][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:52,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:53,029][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:53,821][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:54,617][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:55,412][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:56,205][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:56,997][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:57,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:58,586][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:30:59,379][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:00,173][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:00,967][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:01,759][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:02,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:03,919][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:04,714][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:05,510][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:06,303][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:07,096][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:07,892][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:08,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:09,482][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:10,276][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:11,072][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:11,868][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:12,661][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:14,300][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:31:15,219][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:31:15,220][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:31:16,617][__main__][INFO] - Iteration 991 took 54s (37.04% Gen, 62.95% Train). Generation: 20s, Training: 34s. Estimated remaining time: -593s. Estimated total time: 15h 10m 51s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 5s, 500 more iterations: 7h 35m 25s. +[2025-08-20 23:31:16,618][__main__][INFO] - Starting iteration 991. +[2025-08-20 23:31:39,797][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:31:39,798][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:31:39,804][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:31:42,281][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:31:42,282][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:31:42,289][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:31:42,291][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:31:42,292][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:31:42,590][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:43,378][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:44,170][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:44,962][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:45,757][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:46,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:47,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:48,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:48,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:49,721][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:50,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:51,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:52,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:52,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:53,693][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:54,485][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:55,279][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:56,496][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:57,287][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:58,077][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:58,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:31:59,663][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:00,456][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:01,248][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:02,039][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:02,833][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:03,626][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:04,418][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:05,213][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:06,005][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:06,796][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:07,592][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:09,178][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:32:10,118][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:32:10,120][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:32:11,477][__main__][INFO] - Iteration 992 took 54s (37.73% Gen, 62.27% Train). Generation: 20s, Training: 34s. Estimated remaining time: -442s. Estimated total time: 15h 14m 18s. Time estimates for 10 more iterations: 9m 8s, 100 more iterations: 1h 31m 25s, 500 more iterations: 7h 37m 9s. +[2025-08-20 23:32:11,478][__main__][INFO] - Starting iteration 992. +[2025-08-20 23:32:35,447][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:32:35,449][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:32:35,455][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:32:37,919][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:32:37,921][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:32:37,927][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:32:37,929][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:32:37,930][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:32:38,231][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:39,022][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:39,814][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:40,605][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:41,400][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:42,192][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:42,985][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:43,781][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:44,574][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:45,367][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:46,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:46,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:47,749][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:48,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:49,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:50,130][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:50,923][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:51,720][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:52,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:53,886][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:54,681][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:55,476][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:56,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:57,062][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:57,858][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:58,653][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:32:59,446][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:00,240][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:01,037][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:01,832][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:02,625][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:03,419][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:05,014][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:33:05,919][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:33:05,920][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:33:07,233][__main__][INFO] - Iteration 993 took 55s (38.56% Gen, 61.43% Train). Generation: 21s, Training: 34s. Estimated remaining time: 6m 37s. Estimated total time: 15h 29m 13s. Time estimates for 10 more iterations: 9m 17s, 100 more iterations: 1h 32m 55s, 500 more iterations: 7h 44m 36s. +[2025-08-20 23:33:07,234][__main__][INFO] - Starting iteration 993. +[2025-08-20 23:33:30,439][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:33:30,440][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:33:30,447][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:33:32,916][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:33:32,918][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:33:32,924][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:33:32,926][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:33:32,927][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:33:33,225][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:34,015][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:34,807][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:35,601][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:36,394][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:37,188][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:37,984][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:38,779][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:39,572][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:40,366][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:41,163][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:41,957][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:42,751][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:43,546][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:44,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:45,135][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:45,928][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:46,725][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:47,522][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:48,317][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:49,111][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:50,451][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:51,245][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:52,042][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:52,838][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:53,631][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:54,426][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:55,223][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:56,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:56,817][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:57,613][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:33:58,407][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:00,056][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:34:01,000][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:34:01,002][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:34:02,242][__main__][INFO] - Iteration 994 took 55s (37.70% Gen, 62.30% Train). Generation: 20s, Training: 34s. Estimated remaining time: -403s. Estimated total time: 15h 16m 47s. Time estimates for 10 more iterations: 9m 10s, 100 more iterations: 1h 31m 40s, 500 more iterations: 7h 38m 23s. +[2025-08-20 23:34:02,244][__main__][INFO] - Starting iteration 994. +[2025-08-20 23:34:24,974][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:34:24,975][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:34:24,982][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:34:27,424][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:34:27,425][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:34:27,432][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:34:27,434][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:34:27,435][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:34:27,733][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:28,523][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:29,312][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:30,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:30,894][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:31,685][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:32,477][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:33,268][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:34,060][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:34,851][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:35,645][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:36,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:37,227][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:38,020][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:38,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:40,047][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:40,841][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:41,635][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:42,428][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:43,224][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:44,018][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:44,811][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:45,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:46,404][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:47,199][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:47,992][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:48,786][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:49,583][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:50,380][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:51,176][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:51,970][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:52,765][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:34:54,350][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:34:55,302][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:34:55,304][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:34:56,700][__main__][INFO] - Iteration 995 took 54s (37.23% Gen, 62.77% Train). Generation: 20s, Training: 34s. Estimated remaining time: -1009s. Estimated total time: 15h 7m 35s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 45s, 500 more iterations: 7h 33m 47s. +[2025-08-20 23:34:56,702][__main__][INFO] - Starting iteration 995. +[2025-08-20 23:35:19,544][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:35:19,546][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:35:19,552][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:35:22,008][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:35:22,009][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:35:22,016][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:35:22,018][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:35:22,019][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:35:22,319][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:23,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:23,897][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:24,688][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:25,478][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:26,269][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:27,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:27,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:28,644][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:29,435][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:30,228][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:31,021][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:31,812][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:32,607][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:33,399][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:34,724][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:35,517][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:36,309][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:37,104][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:37,898][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:38,692][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:39,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:40,285][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:41,078][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:41,871][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:42,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:43,463][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:44,258][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:45,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:45,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:46,646][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:47,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:35:49,079][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:35:50,070][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:35:50,071][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:35:51,355][__main__][INFO] - Iteration 996 took 54s (37.28% Gen, 62.72% Train). Generation: 20s, Training: 34s. Estimated remaining time: -867s. Estimated total time: 15h 10m 52s. Time estimates for 10 more iterations: 9m 6s, 100 more iterations: 1h 31m 5s, 500 more iterations: 7h 35m 26s. +[2025-08-20 23:35:51,356][__main__][INFO] - Starting iteration 996. +[2025-08-20 23:36:14,781][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:36:14,782][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:36:14,788][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:36:17,242][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:36:17,243][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:36:17,250][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:36:17,253][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:36:17,253][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:36:17,552][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:18,342][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:19,136][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:19,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:20,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:21,516][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:22,313][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:23,108][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:23,902][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:24,695][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:25,492][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:26,286][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:27,080][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:27,875][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:28,672][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:29,467][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:30,261][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:31,055][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:31,852][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:32,648][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:33,442][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:34,236][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:35,547][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:36,340][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:37,134][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:37,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:38,728][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:39,524][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:40,318][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:41,113][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:41,910][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:42,707][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:36:44,362][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:36:45,312][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:36:45,314][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:36:46,668][__main__][INFO] - Iteration 997 took 55s (37.93% Gen, 62.07% Train). Generation: 20s, Training: 34s. Estimated remaining time: -264s. Estimated total time: 15h 21m 51s. Time estimates for 10 more iterations: 9m 13s, 100 more iterations: 1h 32m 11s, 500 more iterations: 7h 40m 55s. +[2025-08-20 23:36:46,669][__main__][INFO] - Starting iteration 997. +[2025-08-20 23:37:09,427][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:37:09,428][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:37:09,434][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:37:11,892][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:37:11,893][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:37:11,900][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:37:11,902][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:37:11,903][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:37:12,201][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:12,993][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:13,784][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:14,578][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:15,371][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:16,162][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:16,956][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:17,750][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:18,543][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:19,337][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:20,133][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:20,926][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:21,719][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:22,515][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:23,310][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:24,103][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:24,896][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:26,105][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:26,899][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:27,694][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:28,489][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:29,282][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:30,075][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:30,872][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:31,667][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:32,461][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:33,256][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:34,052][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:34,848][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:35,642][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:36,436][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:37,233][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:37:38,820][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:26, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:37:39,773][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:37:39,774][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:37:41,256][__main__][INFO] - Iteration 998 took 54s (37.18% Gen, 62.81% Train). Generation: 20s, Training: 34s. Estimated remaining time: -1044s. Estimated total time: 15h 9m 46s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 58s, 500 more iterations: 7h 34m 53s. +[2025-08-20 23:37:41,257][__main__][INFO] - Starting iteration 998. +[2025-08-20 23:38:04,008][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:04,009][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:04,016][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:06,483][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:06,485][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:06,491][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:06,493][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:38:06,494][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:38:06,793][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:07,582][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:08,376][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:09,167][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:09,961][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:10,756][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:11,548][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:12,341][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:13,137][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:13,930][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:14,723][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:15,519][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:16,314][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:17,107][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:17,900][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:18,696][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:19,490][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:20,284][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:21,079][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:21,876][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:23,171][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:23,965][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:24,758][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:25,554][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:26,349][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:27,143][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:27,937][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:28,734][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:29,531][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:30,324][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:31,118][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:31,914][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:38:33,540][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:38:34,487][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:38:34,488][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:38:35,845][__main__][INFO] - Iteration 999 took 54s (37.15% Gen, 62.85% Train). Generation: 20s, Training: 34s. Estimated remaining time: -1096s. Estimated total time: 15h 9m 47s. Time estimates for 10 more iterations: 9m 5s, 100 more iterations: 1h 30m 58s, 500 more iterations: 7h 34m 53s. +[2025-08-20 23:38:35,847][__main__][INFO] - Starting iteration 999. +[2025-08-20 23:38:58,539][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:58,540][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:38:58,547][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:39:01,021][mllm.training.trainer_ad_align][INFO] - For task: Create alternative trajectory batch +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:39:01,022][mllm.training.trainer_ad_align][INFO] - For task: Get advantages with critic gradient accumulation +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:39:01,028][mllm.training.trainer_ad_align][INFO] - For task: Compute alternative advantage estimates +, + ΔVRAM Allocated: 0.0 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:00, + Percentage of VRAM taken: 69.91574385208075%, + +[2025-08-20 23:39:01,031][mllm.training.trainer_ad_align][INFO] - Sharing advantage alignment data. +[2025-08-20 23:39:01,031][mllm.training.trainer_ad_align][INFO] - Receiving advantage packets. +[2025-08-20 23:39:01,329][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:02,121][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:02,912][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:03,705][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:04,501][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:05,294][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:06,087][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:06,883][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:07,677][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:08,470][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:09,264][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:10,061][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:10,855][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:11,647][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:12,441][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:13,235][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:14,028][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:14,822][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:15,620][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:16,416][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:17,210][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:18,003][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:18,800][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:19,597][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:20,392][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:21,186][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:21,981][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:23,255][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:24,049][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:24,842][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:25,638][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:26,434][mllm.training.trainer_common][INFO] - Accumulated the policy gradient loss for 8960 tokens. +[2025-08-20 23:39:28,070][mllm.training.trainer_common][INFO] - For task: Apply reinforce step +, + ΔVRAM Allocated: 2.288818359375e-05 GB, + ΔVRAM Reserved: 0.0 GB, + ΔTime: 00:00:27, + Percentage of VRAM taken: 69.91579512747062%, + +[2025-08-20 23:39:29,001][mllm.training.trainer_common][INFO] - Saved main optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/policy_optimizer_state.pt +[2025-08-20 23:39:29,003][mllm.training.trainer_common][INFO] - Saved critic optimizer state to /network/scratch/m/mohammed.muqeeth/llm_negotiation/2025_08/ipd_prev_ad_align_qwen2.5_7b/seed_1000/agent_trainer/critic_optimizer_state.pt +[2025-08-20 23:39:30,310][__main__][INFO] - Iteration 1000 took 54s (37.14% Gen, 62.86% Train). Generation: 20s, Training: 34s. Estimated remaining time: -1276s. Estimated total time: 15h 7m 42s. Time estimates for 10 more iterations: 9m 4s, 100 more iterations: 1h 30m 46s, 500 more iterations: 7h 33m 51s. diff --git a/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/README.md b/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/README.md new file mode 100644 index 0000000000000000000000000000000000000000..97bbe5f4f6ec9cdf36c95d565ec19141d4ac1137 --- /dev/null +++ b/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/README.md @@ -0,0 +1,207 @@ +--- +base_model: Qwen/Qwen2.5-7B-Instruct +library_name: peft +pipeline_tag: text-generation +tags: +- base_model:adapter:Qwen/Qwen2.5-7B-Instruct +- lora +- transformers +--- + +# Model Card for Model ID + + + + + +## Model Details + +### Model Description + + + + + +- **Developed by:** [More Information Needed] +- **Funded by [optional]:** [More Information Needed] +- **Shared by [optional]:** [More Information Needed] +- **Model type:** [More Information Needed] +- **Language(s) (NLP):** [More Information Needed] +- **License:** [More Information Needed] +- **Finetuned from model [optional]:** [More Information Needed] + +### Model Sources [optional] + + + +- **Repository:** [More Information Needed] +- **Paper [optional]:** [More Information Needed] +- **Demo [optional]:** [More Information Needed] + +## Uses + + + +### Direct Use + + + +[More Information Needed] + +### Downstream Use [optional] + + + +[More Information Needed] + +### Out-of-Scope Use + + + +[More Information Needed] + +## Bias, Risks, and Limitations + + + +[More Information Needed] + +### Recommendations + + + +Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. + +## How to Get Started with the Model + +Use the code below to get started with the model. + +[More Information Needed] + +## Training Details + +### Training Data + + + +[More Information Needed] + +### Training Procedure + + + +#### Preprocessing [optional] + +[More Information Needed] + + +#### Training Hyperparameters + +- **Training regime:** [More Information Needed] + +#### Speeds, Sizes, Times [optional] + + + +[More Information Needed] + +## Evaluation + + + +### Testing Data, Factors & Metrics + +#### Testing Data + + + +[More Information Needed] + +#### Factors + + + +[More Information Needed] + +#### Metrics + + + +[More Information Needed] + +### Results + +[More Information Needed] + +#### Summary + + + +## Model Examination [optional] + + + +[More Information Needed] + +## Environmental Impact + + + +Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). + +- **Hardware Type:** [More Information Needed] +- **Hours used:** [More Information Needed] +- **Cloud Provider:** [More Information Needed] +- **Compute Region:** [More Information Needed] +- **Carbon Emitted:** [More Information Needed] + +## Technical Specifications [optional] + +### Model Architecture and Objective + +[More Information Needed] + +### Compute Infrastructure + +[More Information Needed] + +#### Hardware + +[More Information Needed] + +#### Software + +[More Information Needed] + +## Citation [optional] + + + +**BibTeX:** + +[More Information Needed] + +**APA:** + +[More Information Needed] + +## Glossary [optional] + + + +[More Information Needed] + +## More Information [optional] + +[More Information Needed] + +## Model Card Authors [optional] + +[More Information Needed] + +## Model Card Contact + +[More Information Needed] +### Framework versions + +- PEFT 0.16.0 \ No newline at end of file diff --git a/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/agent_adapter/adapter_config.json b/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/agent_adapter/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..afc6377a410aed7c15f6b1a2973b550e3568cb26 --- /dev/null +++ b/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/agent_adapter/adapter_config.json @@ -0,0 +1,41 @@ +{ + "alpha_pattern": {}, + "auto_mapping": null, + "base_model_name_or_path": "Qwen/Qwen2.5-7B-Instruct", + "bias": "none", + "corda_config": null, + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 64, + "lora_bias": false, + "lora_dropout": 0.0, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": null, + "peft_type": "LORA", + "qalora_group_size": 16, + "r": 32, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "gate_proj", + "v_proj", + "k_proj", + "down_proj", + "q_proj", + "up_proj", + "o_proj" + ], + "task_type": "CAUSAL_LM", + "trainable_token_indices": null, + "use_dora": false, + "use_qalora": false, + "use_rslora": false +} \ No newline at end of file diff --git a/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/critic_adapter/adapter_config.json b/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/critic_adapter/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..afc6377a410aed7c15f6b1a2973b550e3568cb26 --- /dev/null +++ b/seed_1000/Qwen/Qwen2.5-7B-Instruct/adapters/critic_adapter/adapter_config.json @@ -0,0 +1,41 @@ +{ + "alpha_pattern": {}, + "auto_mapping": null, + "base_model_name_or_path": "Qwen/Qwen2.5-7B-Instruct", + "bias": "none", + "corda_config": null, + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 64, + "lora_bias": false, + "lora_dropout": 0.0, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": null, + "peft_type": "LORA", + "qalora_group_size": 16, + "r": 32, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "gate_proj", + "v_proj", + "k_proj", + "down_proj", + "q_proj", + "up_proj", + "o_proj" + ], + "task_type": "CAUSAL_LM", + "trainable_token_indices": null, + "use_dora": false, + "use_qalora": false, + "use_rslora": false +} \ No newline at end of file diff --git a/seed_1000/iteration_000/mgid:14218722_rollout_tree.json b/seed_1000/iteration_000/mgid:14218722_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..c96cc4914bd75db223297b414cb0392beddcc86e --- /dev/null +++ b/seed_1000/iteration_000/mgid:14218722_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 14218722, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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+ } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the 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"agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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"C", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_000/mgid:18462487_rollout_tree.json b/seed_1000/iteration_000/mgid:18462487_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..73dc6231e610ed9c84f0c1bbe87d3b18bb1b1339 --- /dev/null +++ b/seed_1000/iteration_000/mgid:18462487_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 18462487, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true 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"rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": 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"is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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+ } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_000/mgid:22994554_rollout_tree.json b/seed_1000/iteration_000/mgid:22994554_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..7e1f363013f2e012ae9bb86bd872fb961c5107d4 --- /dev/null +++ b/seed_1000/iteration_000/mgid:22994554_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 22994554, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true 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"rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": 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"is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + 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+ } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_000/mgid:27728965_rollout_tree.json b/seed_1000/iteration_000/mgid:27728965_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..54fccf36aaaca42abfd75536f4bd7e992a3c680f --- /dev/null +++ b/seed_1000/iteration_000/mgid:27728965_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 27728965, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true 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"rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_000/mgid:44917299_rollout_tree.json b/seed_1000/iteration_000/mgid:44917299_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..dd48b56c62e6d99f52944ba97e78bad7ad3e5a3b --- /dev/null +++ b/seed_1000/iteration_000/mgid:44917299_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 44917299, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_000/mgid:88453227_rollout_tree.json b/seed_1000/iteration_000/mgid:88453227_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..a68f0b4283e4445ac34bcb3a64d0784060d46f13 --- /dev/null +++ b/seed_1000/iteration_000/mgid:88453227_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 88453227, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/agent:Alice_rewards.csv b/seed_1000/iteration_002/agent:Alice_rewards.csv new file mode 100644 index 0000000000000000000000000000000000000000..1cbf7f6dfe1f3e0855c260e148d7c24208dbd34f --- /dev/null +++ b/seed_1000/iteration_002/agent:Alice_rewards.csv @@ -0,0 +1,64 @@ +1,3,1,3,1,3,1,3,1,3 +1,3,1,3,1,3,1,3,1,3 +1,3,1,3,1,3,1,3,1,3 +1,5,0,1,5,0,1,5,0,1 +1,5,0,1,5,0,1,5,0,1 +1,0,1,0,1,0,1,0,1,0 +1,3,1,3,1,3,1,3,1,3 +1,3,1,3,1,3,1,3,1,3 +1,0,1,0,1,0,1,0,1,0 +1,0,1,0,1,0,1,0,1,0 +1,3,1,3,1,3,1,3,1,3 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0000000000000000000000000000000000000000..b37aefbb57352b16402c9fbc125d29afc5bbd4ba --- /dev/null +++ b/seed_1000/iteration_002/mgid:10163391_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 10163391, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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"C", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:12570573_rollout_tree.json b/seed_1000/iteration_002/mgid:12570573_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..f068fd463f15d80613ce4c61bc317bd52ecb3302 --- /dev/null +++ b/seed_1000/iteration_002/mgid:12570573_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 12570573, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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+ } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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+ } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the 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"agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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"C", + "Bob": "C" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:14507739_rollout_tree.json b/seed_1000/iteration_002/mgid:14507739_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..e55e77400a5ddc7ca568678e07fbafb955e2efe0 --- /dev/null +++ b/seed_1000/iteration_002/mgid:14507739_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 14507739, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:15385149_rollout_tree.json b/seed_1000/iteration_002/mgid:15385149_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..22b6c866ac102d51a57e011c9b92774c56ba06cb --- /dev/null +++ b/seed_1000/iteration_002/mgid:15385149_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 15385149, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:17634631_rollout_tree.json b/seed_1000/iteration_002/mgid:17634631_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..2554e2aff3c01d8ac3ef7d775e7ed1a6a5cb24de --- /dev/null +++ b/seed_1000/iteration_002/mgid:17634631_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 17634631, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:19603302_rollout_tree.json b/seed_1000/iteration_002/mgid:19603302_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..72debf81265d226cf373428fff5d6270bc9b9744 --- /dev/null +++ b/seed_1000/iteration_002/mgid:19603302_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 19603302, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:21478155_rollout_tree.json b/seed_1000/iteration_002/mgid:21478155_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..5b6d8c3a706c6326e33ec1b51bec1ac3e3b06cf1 --- /dev/null +++ b/seed_1000/iteration_002/mgid:21478155_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 21478155, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true 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"rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:23059506_rollout_tree.json b/seed_1000/iteration_002/mgid:23059506_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..674ebf5972c940173738b5192562e52f0d858641 --- /dev/null +++ b/seed_1000/iteration_002/mgid:23059506_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 23059506, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:26327009_rollout_tree.json b/seed_1000/iteration_002/mgid:26327009_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..c554312d7fd4043574d7f9fd2e29c6abb755eb6a --- /dev/null +++ b/seed_1000/iteration_002/mgid:26327009_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 26327009, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:27114898_rollout_tree.json b/seed_1000/iteration_002/mgid:27114898_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..0278117a63e2fcd47c6f5047f534671e5f372161 --- /dev/null +++ b/seed_1000/iteration_002/mgid:27114898_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 27114898, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:30364034_rollout_tree.json b/seed_1000/iteration_002/mgid:30364034_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..35899ec2a205e5b4c0f5e3f97d203a100d97ccb7 --- /dev/null +++ b/seed_1000/iteration_002/mgid:30364034_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 30364034, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:32147583_rollout_tree.json b/seed_1000/iteration_002/mgid:32147583_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..4be5d53e0b3073f4e678b216f0093d4d8c4d51e6 --- /dev/null +++ b/seed_1000/iteration_002/mgid:32147583_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 32147583, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": 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"agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:38054466_rollout_tree.json b/seed_1000/iteration_002/mgid:38054466_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..bf78f191952e33ed4881d6830dc0e088872e6bc3 --- /dev/null +++ b/seed_1000/iteration_002/mgid:38054466_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 38054466, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:57516979_rollout_tree.json b/seed_1000/iteration_002/mgid:57516979_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..8a763c9e9142097ee1a2080b22ea87d83ed0bf5f --- /dev/null +++ b/seed_1000/iteration_002/mgid:57516979_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 57516979, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:87376353_rollout_tree.json b/seed_1000/iteration_002/mgid:87376353_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..97802bfa5acb51a888c33bf04e3c8a5514d1eef0 --- /dev/null +++ b/seed_1000/iteration_002/mgid:87376353_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 87376353, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 5.0, + "Bob": 0.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + 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"time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. 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If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": 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"content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/seed_1000/iteration_002/mgid:95672243_rollout_tree.json b/seed_1000/iteration_002/mgid:95672243_rollout_tree.json new file mode 100644 index 0000000000000000000000000000000000000000..2f45745320f3013254d63007d9cc368927a8e96e --- /dev/null +++ b/seed_1000/iteration_002/mgid:95672243_rollout_tree.json @@ -0,0 +1,6464 @@ +{ + "id": 95672243, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent 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"chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + 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"content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + 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"Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 1, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "main_child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true 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{ + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": 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"content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + ] + } + } + }, + "branches": { + "Alice": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + 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"action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 5, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 6, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 7, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 8, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 3.0, + "Bob": 3.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "C" + } + } + } + }, + "time_step": 9, + "child": null + } + } + } + } + } + } + } + } + } + } + ], + "Bob": [ + { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Welcome to an iterated game. You are playing against another agent.\n\nGame Rules:\n1. In each round, both agents simultaneously choose to either play A or B.\n2. The payoffs for each round are as follows:\n - If both agents play A, both receive 3 points.\n - If both agents play B, both receive 1 points.\n - If one agent plays A and the other plays B, the player who plays A receives 0 points and the player who plays B receives 5 points.\n3. The goal is to maximize your total points over all rounds.\n4. You will see the previous play of the other player after the completion of a round.\n\nIn order to play A, you must write \"\" and nothing else.\nIn order to play B, you must write \"\" and nothing else.\n\nDuring the entire game, you must only write \"\" or \"\". If you write anything else, your response will be considered as an error.\n\nThe game starts now:\n", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 0, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 1, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 2, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 0.0, + "Bob": 5.0 + }, + "info": { + "actions": { + "Alice": "C", + "Bob": "D" + } + } + } + }, + "time_step": 3, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Bob", + "content": "", + "is_state_end": false + } + ], + "info": null + } + }, + "simulation_step_log": { + "rewards": { + "Alice": 1.0, + "Bob": 1.0 + }, + "info": { + "actions": { + "Alice": "D", + "Bob": "D" + } + } + } + }, + "time_step": 4, + "child": { + "step_log": { + "action_logs": { + "Alice": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Alice", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + "role": "assistant", + "agent_id": "Alice", + "content": "", + "is_state_end": false + } + ], + "info": null + }, + "Bob": { + "chat_turns": [ + { + "role": "user", + "agent_id": "Bob", + "content": "Last round, the other agent played .", + "is_state_end": true + }, + { + 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